Academic literature on the topic 'Root cause diagnosis'
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Journal articles on the topic "Root cause diagnosis"
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Seo, Eunsoo, Gulustan Dogan, Tarek Abdelzaher, and Theodore Brown. "Root cause diagnosis in error-propagating networks." Security and Communication Networks 9, no. 11 (January 14, 2016): 1297–308. http://dx.doi.org/10.1002/sec.1415.
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Thornhill, N. F., S. L. Shah, and B. Huang. "Detection of Distributed Oscillations and Root-Cause Diagnosis." IFAC Proceedings Volumes 34, no. 27 (June 2001): 149–54. http://dx.doi.org/10.1016/s1474-6670(17)33583-8.
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Ovsyankina, Alla, Ivan Savchenko, Sergey Sanin, Alexey Semigonov, Dmitriy Kornyukov, and Angelika Gerner. "Diagnosis of soil fungi that cause root rot." IOP Conference Series: Earth and Environmental Science 390 (November 24, 2019): 012018. http://dx.doi.org/10.1088/1755-1315/390/1/012018.
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A´shour, Saja W., and Theyab A. Farhan. "Identification of Diagnosis Fungi that Cause Potato Root Rot." Bionatura Journal 1 1, no. 1 (March 15, 2024): 1–8. http://dx.doi.org/10.21931/bj/2024.01.01.51.
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Results of collecting samples from different regions of Anbar Governorate (Al-Amiriyah, Al-Khalidya, Fallujah, Heet and Ramadi) showed that potato root rot disease is widespread in all regions collected. The results of isolation and phenotypic and molecular diagnosis using the polymerase chain reaction (PCR) technique indicated showed the presence of fungus Rhizoctonia spp., and Fungus Fusarium spp. Accompanying potato root rot disease and the pathogenicity test using radish seeds on water Agar (W.R.) culture media, all tested isolates achieved a significant reduction in radish seed plants compared with control treatment uncontaminated by any of the isolates of fungi, which recorded infection rate 0%. Keywords: Potato Root Rot, Diagnosis, Fungi, Rhizoctonia solani , Fusarium solani.
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Choudhury, M. A. A. Shoukat, Shubharthi Barua, Mir Abdul Karim, and Nahid Sanzida. "Root Cause Diagnosis of Plantwide Disturbance Using Harmonic Analysis." IFAC Proceedings Volumes 42, no. 11 (2009): 297–302. http://dx.doi.org/10.3182/20090712-4-tr-2008.00046.
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Duan, Ping, Tongwen Chen, Sirish L. Shah, and Fan Yang. "Methods for root cause diagnosis of plant-wide oscillations." AIChE Journal 60, no. 6 (March 1, 2014): 2019–34. http://dx.doi.org/10.1002/aic.14391.
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Yuan, Tao, and S. Joe Qin. "Root cause diagnosis of plant-wide oscillations using Granger causality." Journal of Process Control 24, no. 2 (February 2014): 450–59. http://dx.doi.org/10.1016/j.jprocont.2013.11.009.
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Yuan, Tao, and S. Joe Qin. "Root cause diagnosis of plant-wide oscillations using Granger causality." IFAC Proceedings Volumes 45, no. 15 (2012): 160–65. http://dx.doi.org/10.3182/20120710-4-sg-2026.00172.
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Li, Gang, S. Joe Qin, and Tao Yuan. "Data-driven root cause diagnosis of faults in process industries." Chemometrics and Intelligent Laboratory Systems 159 (December 2016): 1–11. http://dx.doi.org/10.1016/j.chemolab.2016.09.006.
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Huang, Xiao, Tong Fang, and Qiang Liu. "Multi-Layer DLV for Quality-Relevant Monitoring and Root Cause Diagnosis." IFAC-PapersOnLine 55, no. 6 (2022): 372–77. http://dx.doi.org/10.1016/j.ifacol.2022.07.157.
Full textDissertations / Theses on the topic "Root cause diagnosis"
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Mdini, Maha. "Anomaly detection and root cause diagnosis in cellular networks." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2019. http://www.theses.fr/2019IMTA0144/document.
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Grâce à l'évolution des outils d'automatisation et d'intelligence artificielle, les réseauxmobiles sont devenus de plus en plus dépendants de la machine. De nos jours, une grandepartie des tâches de gestion de réseaux est exécutée d'une façon autonome, sans interventionhumaine. Dans cette thèse, nous avons focalisé sur l'utilisation des techniques d'analyse dedonnées dans le but d'automatiser et de consolider le processus de résolution de défaillancesdans les réseaux. Pour ce faire, nous avons défini deux objectifs principaux : la détectiond'anomalies et le diagnostic des causes racines de ces anomalies. Le premier objectif consiste àdétecter automatiquement les anomalies dans les réseaux sans faire appel aux connaissancesdes experts. Pour atteindre cet objectif, nous avons proposé un algorithme, Watchmen AnomalyDetection (WAD), basé sur le concept de la reconnaissance de formes (pattern recognition). Cetalgorithme apprend le modèle du trafic réseau à partir de séries temporelles périodiques etdétecte des distorsions par rapport à ce modèle dans le flux de nouvelles données. Le secondobjectif a pour objet la détermination des causes racines des problèmes réseau sans aucuneconnaissance préalable sur l'architecture du réseau et des différents services. Pour ceci, nousavons conçu un algorithme, Automatic Root Cause Diagnosis (ARCD), qui permet de localiser lessources d'inefficacité dans le réseau. ARCD est composé de deux processus indépendants :l'identification des contributeurs majeurs à l'inefficacité globale du réseau et la détection desincompatibilités. WAD et ARCD ont fait preuve d'efficacité. Cependant, il est possible d'améliorerces algorithmes sur plusieurs aspectsWith the evolution of automation and artificial intelligence tools, mobile networks havebecome more and more machine reliant. Today, a large part of their management tasks runs inan autonomous way, without human intervention. In this thesis, we have focused on takingadvantage of the data analysis tools to automate the troubleshooting task and carry it to a deeperlevel. To do so, we have defined two main objectives: anomaly detection and root causediagnosis. The first objective is about detecting issues in the network automatically withoutincluding expert knowledge. To meet this objective, we have proposed an algorithm, WatchmenAnomaly Detection (WAD), based on pattern recognition. It learns patterns from periodic timeseries and detect distortions in the flow of new data. The second objective aims at identifying theroot cause of issues without any prior knowledge about the network topology and services. Toaddress this question, we have designed an algorithm, Automatic Root Cause Diagnosis (ARCD)that identifies the roots of network issues. ARCD is composed of two independent threads: MajorContributor identification and Incompatibility detection. WAD and ARCD have been proven to beeffective. However, many improvements of these algorithms are possible
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Tian, Yue. "On improving estimation of root cause distribution of volume diagnosis." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6653.
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Identifying common root causes of systematic defects in a short time is crucial for yield improvement. Diagnosis driven yield analysis (DDYA) such as Root cause deconvolution (RCD) is a method to estimate root cause distribution by applying statistical analysis on volume diagnosis. By fixing identified common root causes, yield can be improved.
With advanced technologies, smaller feature size and more complex fabrication processes for manufacturing VLSI semiconductor devices lead to more complicated failure mechanisms. Lack of domain knowledge of such failure mechanisms makes identifying the emerging root causes more and more difficult. These root causes include but are not limited to layout pattern (certain prone to fail layout shapes) and cell internal root causes. RCD has proved to have certain degree of success in previous work, however, these root cause are not included and pose a challenge for RCD. Furthermore, complex volume diagnosis brings difficulty in investigation on RCD. To overcome the above challenges to RCD, improvement based on better understanding of the method is desired.
The first part of this dissertation proposes a card game model to create controllable diagnosis data which can be used to evaluate the effectiveness of DDYA techniques. Generally, each DDYA technique could have its own potential issues, which need to be evaluated for future improvement. However, due to limitation of real diagnosis data, it is difficult to, 1. Obtain diagnosis data with sufficient diversity and 2. Isolate certain issues and evaluate them separately. With card game model given correct statistical model parameters, impact of different diagnosis scenarios on RCD are evaluated. Overfitting problem from limited sample size is alleviated by the proposed cross validation method.
In the second part of this dissertation, an enhanced RCD flow based on pre-extract layout patterns is proposed to identify layout pattern root causes. Prone to fail layout patterns are crucial factors for yield loss, but they normally have enormous number of types which impact the effectiveness of RCD. Controlled experiment shows effectiveness of enhanced RCD on both layout pattern root causes and interconnect root causes after extending to layout pattern root causes. Test case from silicon data also validates the proposed flow.
The last part of this dissertation addresses RCD extension to cell internal root causes. Due to limitation of domain knowledge in both diagnosis process and defect behavior, parameters of RCD model are not perfectly accurate. As RCD moves to identify cell internal root causes, such limitation become an unescapable challenge for RCD. Due to inherent characteristics of cell internal root cause, RCD including cell internal root cause faces more difficulty due to less accurate model parameters. Rather than enhancing domain knowledge, supervised learning for more accurate parameters based on training data are proposed to improve accuracy of RCD. Both controlled experiments and real silicon data shows that with parameters learned from supervised learning, accuracy of RCD with cell internal root cause are greatly improved.
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Ky, Joël Roman. "Anomaly Detection and Root Cause Diagnosis for Low-Latency Applications in Time-Varying Capacity Networks." Electronic Thesis or Diss., Université de Lorraine, 2025. http://www.theses.fr/2025LORR0026.
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L'évolution des réseaux a conduit à l'émergence d'applications à faible latence (FL) telles que le cloud gaming (CG) et la réalité virtuelle basée sur le cloud (Cloud VR), qui exigent des conditions réseau strictes, notamment une faible latence et une bande passante élevée. Cependant, les réseaux à capacité variable introduisent des dégradations, telles que du délai, des fluctuations de bande passante et des pertes de paquets, qui peuvent significativement altérer l'expérience utilisateur sur les applications FL. Cette thèse vise à concevoir des méthodologies pour détecter et diagnostiquer les anomalies de performance des applications FL fonctionnant sur des réseaux cellulaires et Wi-Fi. Pour atteindre cet objectif, des bancs d'essai expérimentaux réalistes ont été mis en place pour collecter des bases de données caractérisant les performances du réseau et capturant les indicateurs clés de performance (KPI) des applications CG et Cloud VR dans des environnements 4G et Wi-Fi. Ces données constituent la base de l'évaluation et du développement d'algorithmes de détection d'anomalies et de diagnostic basés sur l'apprentissage automatique. Les principales contributions de cette thèse incluent le développement de CATS, une solution de détection d'anomalies basé sur l'apprentissage contrastif, capable d'identifier efficacement les dégradations de l'expérience utilisateur dans les applications CG tout en restant robuste face à la contamination des données. De plus, cette thèse introduit RAID, un système de diagnostic en deux étapes conçu pour identifier les causes racines des problèmes de performance dans le Cloud VR. RAID a démontré une grande efficacité dans le diagnostic des dégradations Wi-Fi, même avec un nombre limité de données annotées. Les résultats de ce travail font progresser les domaines de la détection d'anomalies et du diagnostic des causes racines, offrant des perspectives concrètes aux opérateurs de réseaux pour optimiser les performances de leurs réseaux et améliorer la fiabilité des services et mieux supporter les applications FL, qui sont appelées à révolutionner les technologies de communication et à stimuler l'innovation dans de nombreuses industriesThe evolution of networks has driven the emergence of low-latency (LL) applications such as cloud gaming (CG) and cloud virtual reality (Cloud VR), which demand stringent network conditions, including low latency and high bandwidth. However, time-varying capacity networks introduce impairments such as delays, bandwidth fluctuations, and packet loss, which can significantly degrade user experience on LL applications. This research aims to design methodologies for detecting and diagnosing performance anomalies in LL applications operating over cellular and Wi- Fi networks. To achieve this, realistic experimental testbeds were established to collect datasets that characterize network performance and capture key performance indicators (KPIs) of CG and Cloud VR applications over 4G and Wi-Fi environments. These datasets serve as the foundation for evaluating and developing machine learning-based anomaly detection and diagnostic frameworks. The key contributions of this thesis include the development of CATS, a contrastive learning-based anomaly detection framework capable of efficiently identifying user experience degradation in CG applications while remaining robust to data contamination. Additionally, this research introduces RAID, a two-stage root causes diagnosis framework designed to pinpoint the root causes of performance issues in Cloud VR. RAID demonstrated high efficiency in diagnosing Wi-Fi impairments, even with limited labeled data. The findings of this work advance the fields of anomaly detection and root cause diagnosis, offering actionable insights for network operators to optimize network performance and enhance service reliability to support LL applications, which are set to revolutionize communication technologies and drive innovation across various industries
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Ibidunmoye, Olumuyiwa. "Performance problem diagnosis in cloud infrastructures." Licentiate thesis, Umeå universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-120287.
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Cloud datacenters comprise hundreds or thousands of disparate application services, each having stringent performance and availability requirements, sharing a finite set of heterogeneous hardware and software resources. The implication of such complex environment is that the occurrence of performance problems, such as slow application response and unplanned downtimes, has become a norm rather than exception resulting in decreased revenue, damaged reputation, and huge human-effort in diagnosis. Though causes can be as varied as application issues (e.g. bugs), machine-level failures (e.g. faulty server), and operator errors (e.g. mis-configurations), recent studies have attributed capacity-related issues, such as resource shortage and contention, as the cause of most performance problems on the Internet today. As cloud datacenters become increasingly autonomous there is need for automated performance diagnosis systems that can adapt their operation to reflect the changing workload and topology in the infrastructure. In particular, such systems should be able to detect anomalous performance events, uncover manifestations of capacity bottlenecks, localize actual root-cause(s), and possibly suggest or actuate corrections. This thesis investigates approaches for diagnosing performance problems in cloud infrastructures. We present the outcome of an extensive survey of existing research contributions addressing performance diagnosis in diverse systems domains. We also present models and algorithms for detecting anomalies in real-time application performance and identification of anomalous datacenter resources based on operational metrics and spatial dependency across datacenter components. Empirical evaluations of our approaches shows how they can be used to improve end-user experience, service assurance and support root-cause analysis.Cloud Control (C0590801)
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Chen, Shaoqiang. "Manufacturing process design and control based on error equivalence methodology." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002511.
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Liu, Gang. "Spatiotemporal Sensing and Informatics for Complex Systems Monitoring, Fault Identification and Root Cause Diagnostics." Scholar Commons, 2015. https://scholarcommons.usf.edu/etd/5727.
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In order to cope with system complexity and dynamic environments, modern industries are investing in a variety of sensor networks and data acquisition systems to increase information visibility. Multi-sensor systems bring the proliferation of high-dimensional functional Big Data that capture rich information on the evolving dynamics of natural and engineered processes. With spatially and temporally dense data readily available, there is an urgent need to develop advanced methodologies and associated tools that will enable and assist (i) the handling of the big data communicated by the contemporary complex systems, (ii) the extraction and identification of pertinent knowledge about the environmental and operational dynamics driving these systems, and (iii) the exploitation of the acquired knowledge for more enhanced design, analysis, monitoring, diagnostics and control.
My methodological and theoretical research as well as a considerable portion of my applied and collaborative work in this dissertation aims at addressing high-dimensional functional big data communicated by the systems. An innovative contribution of my work is the establishment of a series of systematic methodologies to investigate the complex system informatics including multi-dimensional modeling, feature extraction and selection, model-based monitoring and root cause diagnostics.
This study presents systematic methodologies to investigate spatiotemporal informatics of complex systems from multi-dimensional modeling and feature extraction to model-driven monitoring, fault identification and root cause diagnostics. In particular, we developed a multiscale adaptive basis function model to represent and characterize the high-dimensional nonlinear functional profiles, thereby reducing the large amount of data to a parsimonious set of variables (i.e., model parameters) while preserving the information. Furthermore, the complex interdependence structure among variables is identified by a novel self-organizing network algorithm, in which the homogeneous variables are clustered into sub-network communities. Then we minimize the redundancy of variables in each cluster and integrate the new set of clustered variables with predictive models to identify a sparse set of sensitive variables for process monitoring and fault diagnostics. We evaluated and validated our methodologies using real-world case studies that extract parameters from representation models of vectorcardiogram (VCG) signals for the diagnosis of myocardial infarctions. The proposed systematic methodologies are generally applicable for modeling, monitoring and diagnosis in many disciplines that involve a large number of highly-redundant variables extracted from the big data.
The self-organizing approach was also innovatively developed to derive the steady geometric structure of a network from the recurrence-based adjacency matrix. As such, novel network-theoretic measures can be achieved based on actual node-to-node distances in the self-organized network topology.
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Santos, Ricardo Luis dos. "Identificação interativa da causa raiz de problemas no gerenciamento de mudanças de TI." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/56844.
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Atualmente, o ambiente extremamente dinâmico e altamente competitivo, no qual as organizações estão inseridas, aumentou expressivamente a importância da Tecnologia da Informação (TI). Aliada a isto, a crescente complexidade das infraestruturas de redes e serviços, também conhecidas como infraestruturas de TI, tornaram a Gerência de Serviços de Tecnologia da Informação indispensável a qualquer organização. Nesse cenário, coletâneas de boas práticas e processos foram desenvolvidas visando a obter o melhor desempenho dos produtos e serviços oferecidos. Entre as coletâneas amplamente aceitas, tanto na academia quanto na indústria, vale destacar a Biblioteca de Infraestrutura de Tecnologia da Informação (i.e., Information Technology Infrastructure Library, ITIL). Dentre os processos descritos na ITIL, o Gerenciamento de Mudanças é de suma importância para as organizações. Tal processo é responsável por padronizar o planejamento, o agendamento, a implementação e a avaliação de mudanças no ambiente de TI. Ainda que a adoção das boas práticas proporcione significativas melhorias, a ocorrência de falhas em procedimentos de mudanças não pode ser negligenciada. Para tratar eventuais falhas é proposto o processo de Gerenciamento de Problemas, o qual é responsável por gerir o ciclo de vida de problemas. Nesse contexto, o reúso do conhecimento e da experiência do operador, sobre os processos de TI, é de fundamental importância, pois possibilita a simplificação de procedimentos para a detecção da causa raiz de falhas e, consequentemente, minimiza perdas financeiras e custos de manutenção. Apesar disso, nas soluções existentes o processo de diagnóstico é realizado de uma forma estática e ad hoc, o que dificulta reutilizar o conhecimento em falhas recorrentes ou similares. Diante do exposto, nesta dissertação é proposta uma solução para a identificação interativa da causa raiz de problemas detectados em mudanças. Em contraste com as soluções existentes, o foco desta dissertação é o reúso de traços históricos de diagnósticos e da experiência do operador. A solução proposta teve sua aplicabilidade avaliada em dois estudos de casos. Os cenários utilizados são baseados em situações reais, bem como consideram falhas recorrentes em mudanças sobre as infraestruturas de redes e serviços. Os resultados demonstram a capacidade da solução em reutilizar o conhecimento adquirido com experiências anteriores, bem como, na adaptabilidade do diagnóstico gerado, tanto na infraestrutura de TI na qual a falha ocorre, quanto nas respostas fornecidas pelo operador. A estrutura modular da solução desenvolvida permite dividir a complexidade do processo de identificação em problemas menores que podem ser resolvidos individualmente. Assim, as organizações podem personalizar partes da solução para melhor refletirem as necessidades específicas do seu ambiente de TI.Presently, the extremely dynamic and highly competitive environment, in which organizations are inserted, significantly increased the importance of Information Technology (IT). In addition, the increasing complexity of network infrastructures and services, also known as IT infrastructures, turned the Information Technology Service Management essential to any organization. In this context, guides of best practices and processes were developed to obtain a best performance out of provided products and services. Among the most widely accepted proposals, in both academy and industry, it is worth mentioning the ITIL (Information Technology Infrastructure Library). From the processes described in ITIL, Change Management is indispensable for organizations. This process defines how changes should be planned, scheduled, implemented, and evaluated in IT environments. Despite the significant improvements that the adoption of ITIL’s best practices can provide, the occurrence of failures cannot be neglected. To address possible failures, the process of Problem Management is proposed. Such process is responsible for managing the life cycle of problems. Thus, the reuse of operator’s knowledge and experience, on IT processes, has fundamental importance for allowing to simplify the procedures of detecting failures’ root cause and, therefore, minimizes financial losses and maintenance costs. Nevertheless, the diagnostic process, in the existing solutions, is performed in a static and ad hoc fashion, which complicates the reuse of knowledge in recurrent failures or similar. Based on the previous considerations, in this dissertation, we propose a solution for interactive identification of the root cause of problems detected in IT changes. In contrast to existing solutions, the focus of this solution is the reuse of the operator’s experience and the knowledge from cases that have already been completed. The proposed solution had its applicability evaluated in two case studies. The scenarios used are based on real situations, taking into account recurrent failures in changes on IT infrastructures. The results show not only the ability of the solution to reuse the knowledge acquired from previous experiences, but also the adaptability of diagnosis generated in both the IT infrastructure, where failure occurs, and the responses provided by operator. The modular structure of the developed solution allows to divide the complexity of the identification process into smaller problems that can be solved individually. Thus, organizations can customize parts of the solution to better reflect the specific requirements of your IT environment.
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Consciência, Diogo Miguel Camarinha. "Tosse cardiogénica e tosse não-cardiogénica : estudo das alterações de parâmetros ecocardiográficos de 58 canídeos com tosse." Master's thesis, Universidade de Lisboa. Faculdade de Medicina Veterinária, 2014. http://hdl.handle.net/10400.5/6895.
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Dissertação de Mestrado Integrado em Medicina VeterináriaA tosse consiste no som associado a um importante reflexo fisiológico e defensivo das vias respiratórias cuja função é impedir a entrada, remover e expelir substâncias que são nocivas. O reflexo da tosse torna-se um desafio veterinário quando a via reflexa sofre modelações que podem conduzir a uma resposta exagerada e a uma tosse inapropriada, o que comprometerá a vida e o bem-estar do animal. O reflexo tussígeno pode ser estimulado por agentes mecânicos ou químicos que promovam a irritação da laringe, faringe, traqueia, brônquios e vias respiratórias inferiores. A tosse, em pequenos animais, pode ser causada por afeções que se dividem nas seguintes categorias: alérgicas/inflamatórias, traumática/físicas, neoplásicas, cardiovasculares e infeciosas. Geralmente, os canídeos surgem à consulta com queixas de tosse com mais de 8 semanas, ou seja, crónica. A abordagem de um doente com tosse necessita de conjugar uma boa anamnese e caracterização da tosse, um completo exame físico, informação radiográfica e informação ecocardiográfica de forma a distinguir as causas cardíacas das não cardíacas. O exame ecocardiográfico complementa a radiografia mas não a substitui. A imagem radiográfica, além do estudo do campo pulmonar, fornece informação relativamente às dimensões e contorno cardíaco, enquanto a ecocardiografia permite verificar a função do coração bem como a espessura e a forma das suas câmaras cardíacas, válvulas e tecidos moles, permitindo diferencia-las do seu conteúdo sanguíneo. Neste trabalho será revisto o mecanismo da tosse, a sua fisiopatologia, o diagnóstico radiográfico e o diagnóstico ecocardiográfico. Seguidamente é apresentado um estudo das alterações ecocardiográficas verificadas em 58 cães que apresentavam tosse,. Verificou-se que os parâmetros mais sensíveis para distinguir os casos de tosse cardiogénica dos casos de tosse não-cardiogénica foram o coeficiente AE/Ao, que está aumentado nos casos de IVM e CMD, e o EPSS que se encontra aumentado nos casos de CMD. O FES não foi considerado por ter baixa sensibilidade e significância. Com base nestes parâmetros traçou-se uma árvore de decisão que diferencia a tosse cardíaca causada por IVM e CMD das tosses não-cardiogénicas. Com este estudo concluiu-se que a tosse cardiogénica está relacionada com um aumento cardíaco e que a abordagem necessita de estabelecer uma relação entre os dados obtidos pelas medições ecocardiográficas, doppler, radiografia e todo o historial do animal.
ABSTRACT - patient with cough requires a good anamnesis and characterization of cough, a complete physical examination, radiological and echocardiographic information in order to distinguish cardiac and non-cardiac causes. Doppler echocardiographic examination complements the x-ray but does not replace it. Beyond the study of the lung field, radiological image provides information regarding dimensions and shapes, while cardiac echo allows checking the heart’s function as well as the thickness and shape of its chambers and soft tissues, allowing the differentiation from their blood content. This manuscript will review the mechanism of cough, its pathophysiology, radiological diagnosis and echocardiographic diagnosis. Afterwards, a study is presented comprising the echocardiographic changed findings verified in 58 dogs with cough. It was found that the most sensitive parameters to distinguish cardiogenic from non-cardiogenic cough cases were the Left Atrium to Aorta coefficient, which is increased in cases of mitral valve failure and dilated cardiomyopathy, and the E-Point to Septal Separation which is increased in cases of dilated cardiomyopathy. The Systolic Shortening Fraction was not considered due to low sensitivity and significance. Based on these parameters, a decision tree was drawn, allowing to differentiate cardiac cough caused by mitral valve failure and dilated cardiomyopathy from non-cardiogenic cough. This study concluded that cardiac cough is associated with heart enlargement and that the approach needs to establish a relationship between the data obtained by measurement, doppler echocardiographic findings, radiograph and the entire animal’s history.
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Zhang, Mei. "Diagnostic de panne et analyse des causes profondes du système dynamique inversible." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30260/document.
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Beaucoup de services vitaux de la vie quotidienne dépendent de systèmes d'ingénierie hautement complexes et interconnectés; Ces systèmes sont constitués d'un grand nombre de capteurs interconnectés, d'actionneurs et de composants du système. L'étude des systèmes interconnectés joue un rôle important dans l'étude de la fiabilité des systèmes dynamiques; car elle permet d'étudier les propriétés d'un système interconnecté en analysant ses sous-composants moins complexes. Le diagnostic des pannes est essentiel pour assurer des opérations sûres et fiables des systèmes de contrôle interconnectés. Dans toutes les situations, le système global et / ou chaque sous-système peuvent être analysés à différents niveaux pour déterminer la fiabilité du système global. Dans certains cas, il est important de déterminer les informations anormales des variables internes du sous-système local, car ce sont les causes qui contribuent au fonctionnement anormal du processus global. Cette thèse porte sur les défis de l'application de la théorie inverse du système et des techniques FDD basées sur des modèles pour traiter le problème articulaire du diagnostic des fautes et de l'analyse des causes racines (FD et RCA). Nous étudions ensuite le problème de l'inversibilité de la gauche, de l'observabilité et de la diagnosticabilité des fauts du système interconnecté, formant un algorithme FD et RCA multi-niveaux basé sur un modèle. Ce système de diagnostic permet aux composants individuels de surveiller la dynamique interne localement afin d'améliorer l'efficacité du système et de diagnostiquer des ressources de fautes potentielles pour localiser un dysfonctionnement lorsque les performances du système global se dégradent. Par conséquent, un moyen d'une combinaison d'intelligence locale avec une capacité de diagnostic plus avancée pour effectuer des fonctions FDD à différents niveaux du système est fourni. En conséquence, on peut s'attendre à une amélioration de la localisation des fauts et à de meilleurs moyens de maintenance prédictive. La nouvelle structure du système, ainsi que l'algorithme de diagnostic des fautes, met l'accent sur l'importance de la RCA de défaut des dispositifs de terrain, ainsi que sur l'influence de la dynamique interne locale sur la dynamique globale. Les contributions de cette thèse sont les suivantes: Tout d'abord, nous proposons une structure de système non linéaire interconnecté inversible qui garantit le fauts dans le sous-système de périphérique de terrain affecte la sortie mesurée du système global de manière unique et distincte. Une condition nécessaire et suffisante est développée pour assurer l'inversibilité du système interconnecté qui nécessite l'inversibilité de sous-systèmes individuels. Deuxièmement, un observateur interconnecté à deux niveaux est développé; Il se compose de deux estimateurs d'état, vise à fournir des estimations précises des états de chaque sous-système, ainsi que l'interconnexion inconnue. En outre, il fournira également une condition initiale pour le reconstructeur de données et le filtre de fauts local une fois que la procédure FD et RCA est déclenchée par tout fauts. D'une part, la mesure utilisée dans l'estimateur de l'ancien sous-système est supposée non accessible; La solution est de la remplacer par l'estimation fournie par l'estimateur de ce dernier sous-systèmeMany of the vital services of everyday life depend on highly complex and interconnected engineering systems; these systems consist of large number of interconnected sensors, actuators and system components. The study of interconnected systems plays a significant role in the study of reliability theory of dynamic systems, as it allows one to investigate the properties of an interconnected system by analyzing its less complicated subcomponents. Fault diagnosis is crucial in achieving safe and reliable operations of interconnected control systems. In all situations, the global system and/or each subsystem can be analyzed at different levels in investigating the reliability of the overall system; where different levels mean from system level down to the subcomponent level. In some cases, it is important to determine the abnormal information of the internal variables of local subsystem, in order to isolate the causes that contribute to the anomalous operation of the overall process. For example, if a certain fault appears in an actuator, the origin of that malfunction can have different causes: zero deviation, leakage, clogging etc. These origins can be represented as root cause of an actuator fault. This thesis concerns with the challenges of applying system inverse theory and model based FDD techniques to handle the joint problem of fault diagnosis & root cause analysis (FD & RCA) locally and performance monitoring globally. By considering actuator as individual dynamic subsystem connected with process dynamic subsystem in cascade, we propose an interconnected nonlinear system structure. We then investigate the problem of left invertibility, fault observability and fault diagnosability of the interconnected system, forming a novel model based multilevel FD & RCA algorithm. This diagnostic algorithm enables individual component to monitor internal dynamics locally to improve plant efficiency and diagnose potential fault resources to locate malfunction when operation performance of global system degrades. Hence, a means of acombination of local intelligence with a more advanceddiagnostic capability (combining fault monitoring anddiagnosis at both local and global levels) to performFDDfunctions on different levels of the plantis provided. As a result, improved fault localization and better predictive maintenance aids can be expected. The new system structure, together with the fault diagnosis algorithm, is the first to emphasize the importance of fault RCA of field devices, as well as the influences of local internal dynamics on the global dynamics. The developed model based multi-level FD & RCA algorithm is then a first effort to combine the strength of the system level model based fault diagnosis with the component level model based fault diagnosis. The contributions of this thesis include the following: Firstly, we propose a left invertible interconnected nonlinear system structure which guarantees that fault occurred in field device subsystem will affect the measured output of the global system uniquely and distinguishably. A necessary and sufficient condition is developed to ensure invertibility of the interconnected system which requires invertibility of individual subsystems. Second, a two level interconnected observer is developed which consists of two state estimators, aims at providing accurately estimates of states of each subsystem, as well as the unknown interconnection. In addition, it will also provide initial condition for the input reconstructor and local fault filter once FD & RCA procedure is triggered by any fault. Two underlyingissues are worth to be highlighted: for one hand, the measurement used in the estimator of the former subsystem is assumed not accessible; the solution is to replace it by the estimate provided by the estimator of the latter subsystem. In fact, this unknown output is the unknown interconnection of the interconnected system, and also the input of the latter subsystem
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Zhu, Qin. "Adaptive root cause analysis and diagnosis." Thesis, 2010. http://hdl.handle.net/1828/3153.
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In this dissertation we describe the event processing autonomic computing reference architecture (EPACRA), an innovative reference architecture that solves many important problems related to adaptive root cause analysis and diagnosis (RCAD). Along with the research progress for defining EPACRA, we also identified a set of autonomic computing architecture patterns and proposed a new information seeking model called net-casting model.
EPACRA is important because today, root cause analysis and diagnosis (RCAD) in enterprise systems is still largely performed manually by experienced system administrators. The goal of this research is to characterize, simplify, improve, and automate RCAD processes to ease selected tasks for system administrators and end-users. Research on RCAD processes involves three domains: (1) autonomic computing architecture patterns, (2) information seeking models, and (3) complex event processing (CEP) technologies. These domains as well as existing technologies and standards contribute to the synthesized knowledge of this dissertation.
To minimize human involvement in RCAD, we investigated architecture patterns to be utilized in RCAD processes. We identified a set of autonomic computing architecture patterns and analyzed the interactions among the feedback loops in these individual architecture patterns and how the autonomic elements interact with each other. By illustrating the architecture patterns, we recognized ambiguity in the aggregator-escalator-peer pattern. This problem has been solved by adding a new architecture pattern, namely the chain-of-monitors pattern, to the lattice of autonomic computing architecture patterns.
To facilitate the autonomic information seeking process, we developed the net-casting information seeking model. After identifying the commonalities among three traditional information seeking models, we defined the net-casting model as a five stage process and then tailored it to describe our automated RCAD process.
One of the main contributions of this dissertation is an innovative autonomic computing reference architecture called event processing autonomic computing reference architecture (EPACRA). This reference architecture is based on (1) complex event processing (CEP) concepts, (2) autonomic computing architecture patterns, (3) real use-case workflows, and (4) our net-casting information seeking model. This reference architecture can be leveraged to relieve the system administrator’s burden of routinely performing RCAD tasks in a heterogeneous environment. EPACRA can be viewed as a variant of the IBM ACRA model—extended with CEP to deal with large event clouds in real-time environments. In the middle layer of the reference model, EPACRA introduces an innovative design referred to as use-case-unit—a use case is the scenario of an RCAD process initiated by a symptom—event processing network (EPN) for RCAD. Each use-case-unit EPN reflects our automation approach, including identification of events from the use cases and classifying those events into event types. Apart from defining individual event processing agents (EPAs) to process the different types of events, dynamically constructing use-case unit EPNs is also an innovative approach which may lead to fully autonomic RCAD systems in the future.
Finally, this dissertation presents a case study for EPACRA. As a case study we use a prototype of a Web application intrusion detection tool to demonstrate the autonomic mechanisms of our RCAD process. Specifically, this tool recognizes two types of malicious attacks on web application systems and then takes actions to prevent intrusion attempts. This case study validates both our chain-of-monitors autonomic architecture pattern and our net-casting model. It also validates our use-case-unit EPN approach as an innovative approach to realizing RCAD workflows. Hopefully, this research platform will be beneficial for other RCAD projects and researchers with similar interests and goals.
Books on the topic "Root cause diagnosis"
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Tree Root Damage to Buildings: Causes, Diagnosis and Remedy. Willowmead Publishing Ltd, 1998.
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Cohen, Jeffrey A., Justin J. Mowchun, Victoria H. Lawson, and Nathaniel M. Robbins. A 61-Year-Old Male with Severe Shoulder and Cervical Pain. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190491901.003.0007.
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Severe shoulder pain in the absence of a clear orthopedic cause may be due to acute brachial plexitis. Numbness and tingling in association with weakness and muscle atrophy that cannot be accounted for by a single nerve or nerve root distribution suggests the diagnosis. Additional clues suggesting brachial plexitis include intensity of shoulder pain and antecedent events such as illness, vaccination, injury, unusual physical activity or surgery. The approach to diagnosis of plexitis/plexopathy and appropriate evaluation for etiology are discussed. Management of this condition is conservative, relating to pain control and judicious use of mobilization and strengthening with physical therapy. Prognosis is generally good with recovery of strength occurring in weeks to months.
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Conradie, Ernst M. Redeeming Sin? The Rowman & Littlefield Publishing Group,Inc., 2017. https://doi.org/10.5040/9781978725577.
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Can Christian sin-talk be retrieved within the public sphere? In this contribution to ecotheology, Ernst M. Conradie argues that, amid ecological destruction, discourse on sin can contribute to a multidisciplinary depth diagnosis of what has gone wrong in the world. He confronts some major obstacles related to the plausibility of sin-talk in conversation with evolutionary biology, the cognitive sciences, and animal ethology. He defends an Augustinian insistence that social evil, rather than natural evil, is our primary predicament. If the root cause of social evil is sin, then a Christian confession of sin may yet yield good news for the whole earth.
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Fletcher, Nicholas. Tremor, ataxia, and cerebellar disorders. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780198569381.003.0898.
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Tremors are characterized by rhythmic oscillations of one or more body parts. Although typically seen in the upper limbs, almost any area may be involved, including the trunk, head, facial muscles, and legs. Sometimes, tremor is not visible at all but may be heard or palpated, for example, in vocal or orthostatic tremor, respectively. In neurological practice, the diagnosis and treatment of tremor is an everyday problem. A common scenario is the distinction between essential tremor and Parkinson’s disease. In this chapter, the wide range of tremors are discussed, with their aetiolology, pathophysiology, diagnosis and management described.Ataxia is a term used to describe a wide range of neurological disorders affecting muscle coordination, speech and balance that reflect dysfunction of a part of the central nervous system involved in motor function. Many of ataxias have a cerebellar pathology as root cause, although it must be remembered that ataxia, clumsiness, disordered ocular motility, dysarthria, and even kinetic or intention tremor are not always caused by cerebellar disease. This chapter describes the wide range of cerebellar disorders and ataxias, as are non-cerebellar ataxias such as Friedreich’s ataxia.
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Stogicza, Agnes, Virtaj Singh, and Andrea Trescot. Neurogenic Thoracic Outlet Syndrome. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190271787.003.0008.
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Neurogenic thoracic outlet syndrome (nTOS) is caused by compression of the brachial plexus as it travels from the exiting nerve roots to the axilla. Its presentation, with varying degrees and distributions of arm and hand pain, paresthesias, and numbness, is often either not recognized or is confused with other conditions. Delay in diagnosis causes ongoing suffering for patients, with a concomitant increased use of healthcare services. Imaging and electrodiagnostic studies are often unremarkable, and therefore the diagnosis is based on a detailed medical history, a thorough physical exam, and diagnostic injections. Treatment options are available and can lead to significantly improved quality of life for the patient. Increased awareness of nTOS will likely contribute to its proper diagnosis and treatment.
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Mill, Fredrick. Cystic Fibrosis: A Comprehensive Guide on the Disorders, Diagnosis, Root Causes, Medications and Treatment of Cystic Fibrosis. Independently Published, 2022.
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Croskerry, Pat. The Cognitive Autopsy. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780190088743.001.0001.
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Behind heart disease and cancer, medical error is now listed as one of the leading causes of death. Of the medical errors that lead to injury and death, diagnostic failure is regarded as the most significant. Generally, the majority of diagnostic failures are attributed to the clinicians directly involved with the patient, and to a lesser extent, the system in which they work. In turn, the majority of errors made by clinicians is due to decision making failures manifested by various departures from rationality. Of all the medical environments in which patients are seen and diagnosed, the emergency department is the most challenging. It has been described as a ‘wicked’ environment where illness and disease may range from minor ailments and complaints to severe, life-threatening disorders. The Cognitive Autopsy is a novel strategy towards understanding medical error and diagnostic failure in 42 clinical cases with which the author was directly involved or became aware of at the time. Essentially, it describes a cognitive approach towards root cause analysis of medical adverse events or near misses. Whereas root cause analysis typically focuses on the observable and measurable aspects of adverse events, the cognitive autopsy attempts to identify covert cognitive processes that may have contributed to outcomes. In this clinical setting, no cognitive process is directly observable but must be inferred from the behaviour of the individual clinician. The book illustrates unequivocally that chief among these cognitive processes are cognitive biases and other flaws in decision making, rather than knowledge deficits.
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D, Sheetal DeCaria M. Break the Chronic Pain Cycle: A 90-Day Program to Diagnose and Eliminate the Root Cause of Pain. Megrina Publishing, 2020.
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Fairbank, Jeremy. Management of nerve root pain (syn: sciatica, radicularpain). Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199550647.003.003007.
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♦ Radicular pain can be diagnosed clinically and confirmed by imaging♦ Pain caused by disc herniation can be very severe, but often resolves without intervention♦ Surgery is often successful if non-operative treatment fails.
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Tree Root Damage to Buildings: Causes, Diagnosis and Remedy / Patterns of Soil Drying in Proximity to Trees on Clay Soils. Willowmead Publishing Ltd, 1998.
Find full textBook chapters on the topic "Root cause diagnosis"
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Ye, Fangming, Zhaobo Zhang, Krishnendu Chakrabarty, and Xinli Gu. "Information-Theoretic Syndrome and Root-Cause Evaluation." In Knowledge-Driven Board-Level Functional Fault Diagnosis, 79–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40210-9_5.
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Karra, Srinivas, and M. Nazmul Karim. "Oscillation Root-cause Detection and Quantification Under Multiple Faults." In Detection and Diagnosis of Stiction in Control Loops, 267–93. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-775-2_12.
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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Background." In Intelligent Control and Learning Systems, 1–15. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_1.
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AbstractFault detection and diagnosis (FDD) technology is a scientific field emerged in the middle of the twentieth century with the rapid development of science and data technology. It manifests itself as the accurate sensing of abnormalities in the manufacturing process, or the health monitoring of equipment, sites, or machinery in a specific operating site. FDD includes abnormality monitoring, abnormal cause identification, and root cause location.
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Babu, S., N. Murugan, M. Amudhan, and T. S. Lagnesh. "Fault Diagnosis and Root Cause Failure Analysis of Press Roller Mill for Heavy Industry." In Lecture Notes in Mechanical Engineering, 631–38. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8767-8_53.
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Peng, Piao, Zhiwei Liao, Fushuan Wen, and Jiansheng Huang. "A Root-Cause-Analysis Based Method for Fault Diagnosis of Power System Digital Substations." In Studies in Computational Intelligence, 153–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35638-4_11.
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Passa, Dimitra, Stamatia Gavela, George Papadakos, and George Karydis. "Root-Cause Analysis on the Risk of Process Failure When Testing the Resilience of a Stone Masonry Building." In Advanced Nondestructive and Structural Techniques for Diagnosis, Redesign and Health Monitoring for the Preservation of Cultural Heritage, 156–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03795-5_13.
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Tran, Thierry, Adebayo Abass, Luis Alejandro Taborda Andrade, Arnaud Chapuis, Marcelo Precoppe, Laurent Adinsi, Alexandre Bouniol, et al. "Cost-Effective Cassava Processing: Case Study of Small-Scale Flash-Dryer Reengineering." In Root, Tuber and Banana Food System Innovations, 105–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92022-7_4.
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AbstractThe development and scaling out of flash-dryer innovations for more efficient, small-scale production of high-quality cassava flour (HQCF) and starch is described. The diagnoses of cassava-processing SMEs (small and medium enterprises) revealed their energy expenditures for drying were considerably higher than those of large-scale industrial companies, which was mostly due to suboptimal design of flash-drying systems. As a result, small-scale production of cassava starch and HQCF often incurs high production costs, incompatible with market prices of final products. Taking stock of this situation, RTB scientists have developed several innovations to optimize energy efficiency and costs, including a longer drying pipe, reengineered heat exchanger, larger blower for higher air velocity, and a higher product/air ratio. This was based on numerical modelling to determine the key design features of energy-efficient flash dryers, followed by construction and demonstration of a pilot-scale prototype. As a result, improved small-scale flash dryers are now being scaled out to the private sector in various countries, using the Scaling Readiness framework and achieving 10–15% gains in productivity and incomes. A method for diagnosis of process efficiency is also described, to identify technical bottlenecks and to document and measure the outcomes and impacts during the implementation of scaling-out projects.
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Valade, Dominique. "Emergency Room Headache: A Case with Primary Thunderclap Headache Including Differential Diagnosis from Secondary Ones." In Case-Based Diagnosis and Management of Headache Disorders, 183–88. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06886-2_26.
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Reiz, S. "Diagnosis, Causes, and Treatment of Myocardial Ischemia in the Operating Room." In Clinical Aspects of O2 Transport and Tissue Oxygenation, 162–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83872-9_14.
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Golagha, Mojdeh. "How to Effectively Reduce Failure Analysis Time?" In Ernst Denert Award for Software Engineering 2020, 45–71. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83128-8_4.
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AbstractDebugging is one of the most expensive and challenging phases in the software development life-cycle. One important cost factor in the debugging process is the time required to analyze failures and find underlying faults. Two types of techniques that can help developers to reduce this analysis time are Failure Clustering and Automated Fault Localization. Although there is a plethora of these techniques in the literature, there are still some gaps that prevent their operationalization in real-world contexts. Besides, the abundance of these techniques confuses the developers in selecting a suitable method for their specific domain. In order to help developers in reducing analysis time, we propose methodologies and techniques that can be used standalone or in a form of a tool-chain. Utilizing this tool-chain, developers (1) know which data they need for further analysis, (2) are able to group failures based on their root causes, and (3) are able to find more information about the root causes of each failing group. Our tool-chain was initially developed based on state-of-the-art failure diagnosis techniques. We implemented and evaluated existing techniques. We built on and improved them where the results were promising and proposed new solutions where needed. The overarching goal of this study has been the applicability of techniques in practice.
Conference papers on the topic "Root cause diagnosis"
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Edwards, Mark A., and Ben Cramer. "Top of Line Corrosion – Diagnosis, Root Cause Analysis, and Treatment." In CORROSION 2000, 1–11. NACE International, 2000. https://doi.org/10.5006/c2000-00072.
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Abstract This paper details the processes used to respond to two Top of Line Corrosion (TLC) failures in a sour gas gathering system. The steps used to diagnose and remediate failures, including Root Cause Analysis, modeling of conditions, and changes to operating procedures are presented. The effect of gas cooling rate on water condensation, the pH of the condensed water, and the flow regime in each segment are discussed, as are their effect on the type of corrosion. The effectiveness of batch-filming and continuous inhibition in these lines is presented, as well as changes implemented after the failure incidents to improve corrosion management program effectiveness. Batch filming at proper intervals is shown to be an effective method for preventing TLC.
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Moddemann, Lukas, Henrik Sebastian Steude, Alexander Diedrich, Ingo Pill, and Oliver Niggemann. "Extracting Knowledge using Machine Learning for Anomaly Detection and Root-Cause Diagnosis." In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), 1–8. IEEE, 2024. http://dx.doi.org/10.1109/etfa61755.2024.10710647.
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Van Der Schijff, Ockert J., and Ronald M. Latanision. "Pipeline Condition Assessment – Locating, Diagnosing, and Mitigating Corrosion Damage." In CORROSION 2011, 1–9. NACE International, 2011. https://doi.org/10.5006/c2011-11178.
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Abstract Corrosion of pipe, both from the inside/out and from the outside/in, has been a generic problem that literally “comes with the territory”. Many techniques exist for locating leaks in pipelines, which are applied in industry with varying levels of success. Once located, proper failure analysis and diagnosis of the root cause for the corrosion that resulted in failure of the pipe is a key element for the successful mitigation and prevention of recurrence. This involves a deliberate and programmed approach to investigate all possible causes, comprehensive gathering of field data, and performance of necessary laboratory analyses in order to reach an informed and scientifically justified conclusion. Mechanisms that are known to be associated with localized pipeline corrosion include pitting corrosion, crevice corrosion, microbiologically influenced corrosion (MIC), galvanic corrosion, flow assisted corrosion, and stray current corrosion. These corrosion mechanisms can occur from the inside/out or from the outside/in and may occur at various stages of the deterioration and/or in combination with each other. Once the root cause has been identified, mitigation and prevention techniques and procedures should be based on the outcome of such an analysis. A particular mitigation method, which has proven to be successful in one instance, may not be successful in other circumstances. Every installation has some unique features in terms of environment and possible contributing factors to corrosion, which should be taken into consideration during failure analysis and subsequent identification of mitigation techniques and prevention methodologies. This paper will provide a description of the key elements of such a programmed and integrated failure analysis and diagnosis approach. Case studies of field experience with pipe failures are utilized to illustrate various aspects of this approach, including general and localized corrosion and microbiologically influenced corrosion.
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Hallopeau, Xavier, Arnaud Meillier, Olivier Lesieutre, Elisabeth Marie-Victoire, and Myriam Bouichou. "Cathodic Protection on an Iconic Building of the Modern Movement Architecture." In CORROSION 2021, 1–9. AMPP, 2021. https://doi.org/10.5006/c2021-16599.
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Abstract The repair of a French building classified as an historic monument must meet specific requirements, in particular very precise rules within the framework of the scientific and technical control exercised by the services of the French Ministry of Culture. Repair work must not alter the general appearance of the building or the nature of the materials. In this context, this paper discusses the various operations carried out on an iconic building of the Modernist Architectural Movement situated on the seaside, Villa E-1027 located in Roquebrune-Cap-Martin (06), French Riviera involving: investigation, diagnosis, identification of reinforcing steel corrosion root cause, implementation of a repair strategy, pilot test, installation of cathodic protection by impressed current and results.
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Pérez Armiño, Jorge, and Christopher R. Dyck. "Inspection, Analysis, Diagnosis and Recommendations for Repairs for Two Suspension Bridges in the Patagonia Region of Chile." In IABSE Congress, San José 2024: Beyond Structural Engineering in a Changing World, 218–26. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2024. https://doi.org/10.2749/sanjose.2024.0218.
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<p>The "Senador Sergio Sepulveda" and "Exequiel Gonzalez" bridges are two suspension bridges located in Southern Chile. The bridges are situated on Route 7 that connects Puerto Aysén with Puerto Montt. During the COVID-19 pandemic, border restrictions forced an increase in truck traffic crossing these bridges. This change in traffic patterns placed additional vehicular stress on the structures, leading to fractures in the stiffening truss chords of one of the bridges. The Chilean Ministry of Public Works (MOP) responded by temporarily closing and later reopening the affected bridge after implementing emergency repairs. Once emergency repairs were completed to return the bridge safely to service, a forensic analysis was conducted to understand the root cause of the observed field issues. This paper discusses the inspection, testing, as well as structural analysis conducted and the recommended actions to prevent further structural problems and safely extend the bridges' service life.</p>
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Leach, David G., Wei Wang, Chao Yan, Dillon Mattis, Ron MacLeod, and Wei Wei. "Molecular Deep Dive into Oilfield Microbiologically Influenced Corrosion: a Detailed Case Study of MIC Failure Analysis in an Unconventional Asset." In CONFERENCE 2022, 1–11. AMPP, 2022. https://doi.org/10.5006/c2022-17948.
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Abstract This work details a microbiologically influenced corrosion (MIC) failure analysis case study for a produced water pipeline. A pipeline in a shale and tight asset experienced heavy corrosion and ultimate failure within a 7-month period, with estimated corrosion rate at 161 mils per year (MPY), or 4.1 mm per year (MMPY). Upon removal by the inspection team, heavy white deposit buildup (a suspected microbial biofilm) was observed directly associated with the corrosion failure on top of a black scale underlayer. Detailed assessments were performed using ATP photometry, qPCR speciation, and 16S rRNA gene sequencing to profile the microbial population present, which was dominated by high -risk anaerobic microbial strains such as sulfate-reducing bacteria and methanogens. Scale analysis confirmed iron carbonate and iron sulfides associated with microbial iron metabolism and corrosion, and scanning electron microscopy explored surface morphology. This study will lay out detailed root cause analysis and include best practices for MIC diagnosis and recommendations for future prediction and prevention in oilfield assets.
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Van Der Schijff, Ockert J. "MIC in Fire Sprinkler Systems Field Observations and Data." In CORROSION 2008, 1–8. NACE International, 2008. https://doi.org/10.5006/c2008-08508.
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Abstract Microbiologically Influenced Corrosion (MIC) has been implicated as the root-cause for the corrosion failure of a large number of wet, dry, and pre-action fire sprinkler systems, often resulting in costly repairs and litigation. This paper presents field observations and laboratory test results for several different fire sprinkler failure investigations conducted over the course of the past several years in a variety of locations across the US. Field experience with assessment data, basis for diagnosis, mitigation and rehabilitation recommendations to prevent recurrence are presented. Aggregated results of laboratory microbiological culturing of deposit samples collected from affected sprinkler systems are presented and discussed. Conclusions drawn from these results regarding the part that MIC plays in the corrosion failure of the various types of sprinklers systems are presented and discussed.
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Fairuzov, Yuri, and Victor Fairuzov. "Diagnosis of Internal Corrosion in Pipelines Based on Mapping Adverse Operational Conditions." In CORROSION 2019, 1–15. NACE International, 2019. https://doi.org/10.5006/c2019-13250.
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Abstract Upstream oil and gas companies operate natural gas and crude oil gathering systems comprising flowline networks and process facilities that transport the produced fluids from the wells to a main processing plant. Increasing frequency of corrosion related leaks is a common problem facing oil and gas producers, despite that a corrosion inhibitor is injected into the flowlines. Root-cause analyses conducted by several companies in both conventional and unconventional fields revealed that severe internal corrosion was caused by a low fluid flow velocity, an increasing water cut, and the presence of sulfate-reducing bacteria (SRB) in the production streams. Nevertheless, it was not clear why some of the flowlines leaked (a few lines developed multiple leaks) while others did not leak, despite the composition of produced fluids, principal design parameters (diameter and length), dosage of corrosion inhibitor, and environmental conditions of the flowlines were similar. A diagnostic analysis of flow-induced corrosion in the flowlines was carried out to gain an understanding of different corrosion rates in similar flowlines. The methodology used for the diagnostic analysis comprises 1) Ultra-High Definition simulation of 3-phase or 4-phase flow of gas, oil, water, and solids; 2) 3D imaging of phase distributions inside critical sections of the flowlines as per NACE ICDA methods; 3) mapping adverse operational conditions; and 4) the determination of probability of failure of the flowline sections based on criteria depending on the severity of operating conditions inside and outside the flowlines. It was found that multiple flowline sections were exposed to stagnant water and/or had a fraction of internal surface area covered by a stationary bed of solids (proppant or formation solids produced from the well). The identified causes of potential leaks comprise the following failure mechanisms: a) metal loss caused by colonies of SRB, b) composed load acting on the pipe wall, and c) cyclic" thermal expansion/contraction of the flowlines due to seasonal ambient temperature variations. One of the surprising findings of this study was that a shorter flowline with a lower water cut may have multiple leaks while a longer flowline with a higher water cut may have not a single leak approximately for the same period after commissioning. This result was explained with help of maps of adverse operational conditions constructed for the two groups of flowlines. Immediate corrective mitigation actions and preventive actions were proposed to reduce leak frequency, including the installation of a novel automatic flushing system.
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Brundage, Michael P., Boonserm Kulvatunyou, Toyosi Ademujimi, and Badarinath Rakshith. "Smart Manufacturing Through a Framework for a Knowledge-Based Diagnosis System." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2937.
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Various techniques are used to diagnose problems throughout all levels of the organization within the manufacturing industry. Often times, this root cause analysis is ad-hoc with no standard representation for artifacts or terminology (i.e., no standard representation for terms used in techniques such as fishbone diagrams, 5 why’s, etc.). Once a problem is diagnosed and alleviated, the results are discarded or stored locally as paper/digital text documents. When the same or similar problem reoccurs with different employees or in a different factory, the whole process has to be repeated without taking advantage of knowledge gained from previous problem(s) and corresponding solution(s). When discussing the diagnosis, personnel may miscommunicate over terms used in the root cause analysis leading to wasted time and errors. This paper presents a framework for a knowledge-based manufacturing diagnosis system that aims to alleviate these miscommunications. By learning from diagnosis methods used in manufacturing and in the medical community, this paper proposes a framework which integrates and formalizes root cause analysis by categorizing faults and failures that span multiple organizational levels. The proposed framework aims to enable manufacturing operations by leveraging machine learning and semantic technologies for the manufacturing system diagnosis. A use case for the manufacture of a bottle opener demonstrates the framework.
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Wong, Kenny, Serge Mankovskii, Kostas Kontogiannis, Hausi A. Müller, and John Mylopoulos. "Integrated system diagnosis and root cause analysis." In the 2010 Conference of the Center for Advanced Studies. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1923947.1924029.
Full textReports on the topic "Root cause diagnosis"
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Abdi, Parsa, Christian Awad, Michelle Robert Anthony, and Christopher Farkouh. Effectiveness of Multimodality Therapy using Minoxidil and Microneedling for the Treatment of Alopecia: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2023. http://dx.doi.org/10.37766/inplasy2023.1.0031.
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Review question / Objective: To test the effectiveness of multimodality therapy using minoxidil and micmroneedling, in comparison to minoxodil alone for the treatment of alopecia. In terms of the PICO framework: Population: Includes patients with any form of clinically diagnosed alopecia. Intervention: Includes using combination therapy with microneedling and minoxidil in the treatment of alopecia. Comparison: Includes comparison to minoxidil alone as control group. Outcome: Primary outcome: Increased hair density. Secondary outcome: Increased hair diameter. Condition being studied: Alopecia (hair loss) is a condition that is frequently seen in dermatology. When a thorough examination is made, the root of the issue is frequently revealed, allowing for an explanation and the most suitable treatments. Nevertheless, hair loss can occasionally be the first indicator of a serious underlying medical problem, be observed in conjunction with other conditions, or be a side effect of treatment. Furthermore, alopecia may result in distressingly noticeable symptoms, cause significant patient distress, and cause alopecia with lifelong scars and irreversible hair loss. Therefore, with these illnesses, a precise diagnosis and quick therapy are essential for the most beneficial outcomes.
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Stratton, R. C., and D. B. Jarrell. Towards the development of multilevel-multiagent diagnostic aids. [Root-cause analysis]. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/6128963.
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Miller, D. W., B. K. Hajek, and J. W. Hines. A Diagnostic Hierarchy Approach to Root Cause Analysis for Heavy Water Reactor Malfunction Management. Office of Scientific and Technical Information (OSTI), October 1993. http://dx.doi.org/10.2172/6591.
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Padhye, Suyash, Aishwarya Sharma, Hesam Arefkhani, Deepak Benny, Konstantina Gkritza, Samuel Labi, and Makarand Hastak. INDOT Project Change Orders: Root Causes and Recommendations. Purdue University, 2024. https://doi.org/10.5703/1288284317747.
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This study analyzed the historical trends in change orders (COs) associated with the Indiana Department of Transportation (INDOT) highway contracts. It also investigated correlations between COs and base factors (district, project type, contract type, award amount, geographical area (rural vs. urban)), and sought to understand the effect of the root causes but not to predict CO based on root causes. Unlike the base factors, the root causes are unknown prior to construction. The study developed models that forecasted CO characteristics—CO likelihood, CO frequency (count), and CO severity (magnitude) and CO impacts (cost overrun and schedule delay). To address these research questions, the study used INDOT’s 2010–2020 highway contract data involving approximately 5,000 contracts worth just over $10B. The CO trends and patterns (including root causes) were analyzed for five project types at the six highway administrative districts in Indiana. The study identified the base factors that generally have higher propensity to experience more frequent and severe COs and analyzed CO impacts on cost and schedule overruns. The results indicated that 65% of the contracts had at least 1 CO; 30% had at least 5 COs; 10% had at least 12 COs; and 4% had over 20 COs. The study also analyzed the CO impacts by the dominant root causes: changed conditions (9,544 COs, $266M); errors and omissions (6,507 COs, $139M); scope changes (2,371 COs, $109M), and final quantity adjustments (cost adjustment) ($193M). The top five root causes of the COs in each district were identified and good practices and guidelines to help mitigate the incidence and severity of change orders were developed. The study’s diagnostic analysis and prediction models are useful in project planning and management because they (a) help identify the factors that impact CO likelihood, count, and monetary severity, and (b) can enable more reliable forecasting of project cost and schedule. Overall, the study results can help INDOT achieve its strategic goal of “on-time and on-budget project delivery.” The effective management of change orders aligns with specific provisions that recur across previous and current federal legislation (the Infrastructure Investment and Jobs Act), which emphasize efficient project management and adherence to budgets and timelines in infrastructure development.
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Kaffenberger, Michelle, Jason Silberstein, and Marla Spivack. Evaluating Systems: Three Approaches for Analyzing Education Systems and Informing Action. Research on Improving Systems of Education (RISE), April 2022. http://dx.doi.org/10.35489/bsg-rise-wp_2022/093.
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While conventional interventions and evaluations address the symptoms of the learning crisis, there is growing acknowledgement that widespread and sustained learning improvements will require systems approaches that diagnose and address the root causes of low learning. This paper presents and applies three methods to evaluate education systems and inform how to improve system coherence for learning. First, we use learning trajectories to evaluate the dynamics of children’s learning in 22 low- and middle-income countries. Second, we present a set of principles called the ALIGNS principles and show how they can be used to evaluate and improve alignment of curricula, assessments, and teacher support and instruction. Finally, we present a systems diagnostic framework and apply it to a program in South Africa, showing how the program takes a systems approach to improve learning. These tools help concretize systems thinking and bring insights to bear on the design and evaluation of policies and programs intended to improve learning.
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Rios, Sandra Polónia. MERCOSUR: Dilemas and Alternatives for the Trade Agenda. Inter-American Development Bank, July 2004. http://dx.doi.org/10.18235/0011103.
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This paper formed part of a Conference "Mercosur: In Search of a New Agenda" held in the Getulio Vargas Foundation in Rio de Janeiro in June 2003. The purpose of the Conference was to was to examine future directions for Mercosur in light of emerging political dynamics pointing to renewed interest in deepening the initiative after a turbulent 1999-2002. The papers and the conference were supported by the Integration and Regional Programs Department of the Inter-American Development Bank through its Special Initiative on Trade and Integration. The Department is grateful for the collaboration of the Foundation and in particular to Professor Renato Flores. The recent deterioration of MERCOSUR internal and external credibility stems from decisions taken by its member countries to face domestic and international conflicting issues arising in the last years. Excess of flexibility to lessen differences, a lack of enthusiasm about the bloc¿s deepening agenda, and of a common vision on the role of the integration project in development strategies for the four member countries, have caused distortion to the original project. The root of these divergences can be traced in macroeconomic policies of larger member countries and in the lack of instruments to cope with different economy sizes and to integrate the asymmetric productive structures of the four countries. The purpose of this work is to evaluate issues within the trade agenda of MERCOSUR, assessing its progress and difficulties, identifying the roots of the problems in the integration process and studying main current dilemmas. On the basis of the diagnoses some proposals are presented to be included in the bloc¿s integration agenda for the next years.
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Kistler, Harold Corby, and Talma Katan. Identification of DNA Unique to the Tomato Fusarium Wilt and Crown Rot Pathogens. United States Department of Agriculture, September 1995. http://dx.doi.org/10.32747/1995.7571359.bard.
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Wilt and crown rot are two important diseases of tomato caused by different strains ("formae speciales") of the fungus, Fusarium oxysporum. While both pathogens are members of the same fungal species, each differs genetically and resistance to the diseases is controlled by different genes in the plant. Additionally, the formae speciales differ in their ecology (e.g. optimal temperature of disease development) and epidemiology. Nevertheless, the distinction between these diseases based on symptoms alone may be unclear due to overlapping symptomatology. We have found in our research that the ambiguity of the pathogens is further confounded because strains causing tomato wilt or crown rot each may belong to several genetically and phylogenetically distinct lineages of F. oxysporum. Furthermore, individual lineages of the pathogen causing wilt or crown rot may themselves be very closely related. The diseases share the characteristic that the pathogen's inoculum may be aerially dispersed. This work has revealed a complex evolutionary relationship among lineages of the pathogens that makes development of molecular diagnostic methods more difficult than originally anticipated. However, the degree of diversity found in these soil-borne pathogens has allowed study of their population genetics and patterns of dispersal in agricultural settings.
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Squiers, Linda, Mariam Siddiqui, Ishu Kataria, Preet K. Dhillon, Aastha Aggarwal, Carla Bann, Molly Lynch, and Laura Nyblade. Perceived, Experienced, and Internalized Cancer Stigma: Perspectives of Cancer Patients and Caregivers in India. RTI Press, April 2021. http://dx.doi.org/10.3768/rtipress.2021.rr.0044.2104.
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Cancer stigma may lead to delayed diagnosis and treatment, especially in low- and middle-income countries. This exploratory, pilot study was conducted in India to explore the degree to which cancer stigma is perceived, experienced, and internalized among adults living with cancer and their primary caregivers. We conducted a survey of cancer patients and their caregivers in two Indian cities. The survey assessed perceived, experienced, and internalized stigma; demographic characteristics; patient cancer history; mental health; and social support. A purposive sample of 20 cancer survivor and caregiver dyads was drawn from an ongoing population-based cohort study. Overall, 85 percent of patients and 75 percent of caregivers reported experiencing some level (i.e., yes response to at least one of the items) of perceived, experienced, or internalized stigma. Both patients (85 percent) and caregivers (65 percent) perceived that community members hold at least one stigmatizing belief or attitude toward people with cancer. About 60 percent of patients reported experiencing stigma, and over one-third of patients and caregivers had internalized stigma. The findings indicate that fatalistic beliefs about cancer are prevalent, and basic education about cancer for the general public, patients, and caregivers is required. Cancer-related stigma in India should continue to be studied to determine and address its prevalence, root causes, and influence on achieving physical and mental health-related outcomes.