Thematische Bibliographien / Data-driven maintenance

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Auswahl der wissenschaftlichen Literatur zum Thema „Data-driven maintenance“

Autor: Grafiati

Veröffentlicht am 25. Juni 2021

Zuletzt aktualisiert am 21. Juni 2025

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Zeitschriftenartikel zum Thema "Data-driven maintenance"

Rios, Pablo. "Data-Driven Maintenance." Manufacturing Management 2023, no. 1-2 (2023): 32–33. http://dx.doi.org/10.12968/s2514-9768(23)90381-9.

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Afful-Dadzie, Anthony, and Theodore T. Allen. "Data-Driven Cyber-Vulnerability Maintenance Policies." Journal of Quality Technology 46, no. 3 (2014): 234–50. http://dx.doi.org/10.1080/00224065.2014.11917967.

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Ostrowski, João, and József Menyhárt. "Enhancing maintenance with a data-driven approach." International Review of Applied Sciences and Engineering 10, no. 2 (2019): 135–40. http://dx.doi.org/10.1556/1848.2019.0016.

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Constant stream of data has been generated and stored as more devices are being connected to the internet and supported with cloud technologies. The price drop of such applications along with industry 4.0 trending, triggered an explosive growth and demand for many IT modern solutions. From an industrial point of view, sensorization practices are spreading through factories and warehouses where software is constantly adapting to provide actionable insights in a data-driven configuration. The fourth industrial revolution is empowering the manufacturers with solutions for cost reduction, which translates in competitive advantage. The sector of maintenance operations is leading in engineering innovation, from reactive to planned preventive techniques the next step in history of proactive approaches is Predictive Analytics Maintenance.

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Ma, Zhiliang, Yuan Ren, Xinglei Xiang, and Ziga Turk. "Data-driven decision-making for equipment maintenance." Automation in Construction 112 (April 2020): 103103. http://dx.doi.org/10.1016/j.autcon.2020.103103.

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Wadzuk, Bridget, Bridget Gile, Virginia Smith, Ali Ebrahimian, Micah Strauss, and Robert Traver. "Moving Toward Dynamic and Data-Driven GSI Maintenance." Journal of Sustainable Water in the Built Environment 7, no. 4 (2021): 02521003. http://dx.doi.org/10.1061/jswbay.0000958.

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Lopes Gerum, Pedro Cesar, Ayca Altay, and Melike Baykal-Gürsoy. "Data-driven predictive maintenance scheduling policies for railways." Transportation Research Part C: Emerging Technologies 107 (October 2019): 137–54. http://dx.doi.org/10.1016/j.trc.2019.07.020.

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Coronel, Eduardo, Benjamín Barán, and Pedro Gardel. "A Survey on Data Mining for Data-Driven Industrial Assets Maintenance." Technologies 13, no. 2 (2025): 67. https://doi.org/10.3390/technologies13020067.

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This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. Additionally, the survey proposes four level classes of asset complexity and studies five asset types, including mechanical, electromechanical, electrical, electronic, and computing assets. The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks.

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Wolfartsberger, Josef, Jan Zenisek, and Norbert Wild. "Data-Driven Maintenance: Combining Predictive Maintenance and Mixed Reality-supported Remote Assistance." Procedia Manufacturing 45 (2020): 307–12. http://dx.doi.org/10.1016/j.promfg.2020.04.022.

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Devarajan, Vinodkumar. "Advancing Data Center Reliability Through AI-Driven Predictive Maintenance." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 102–14. https://doi.org/10.37745/ejcsit.2013/vol13n14102114.

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The evolution of data center maintenance has undergone a transformative shift from traditional reactive and scheduled maintenance to AI-driven predictive maintenance strategies. The integration of artificial intelligence and machine learning technologies enables precise failure prediction, optimizes resource allocation, and enhances operational reliability. Advanced sensor networks and sophisticated analytics pipelines process vast amounts of operational data, while machine learning models, including neural networks, support vector machines, and decision trees, provide accurate predictions of component failures. The implementation framework encompasses system integration, data management, model development, and operational integration, leading to substantial improvements in maintenance efficiency, cost reduction, and equipment longevity. The convergence of human expertise with AI capabilities marks a significant advancement in predictive maintenance, revolutionizing how organizations approach data center operations and reliability management.

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Chen, Chuang, Cunsong Wang, Ningyun Lu, Bin Jiang, and Yin Xing. "A data-driven predictive maintenance strategy based on accurate failure prognostics." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 2 (2021): 387–94. http://dx.doi.org/10.17531/ein.2021.2.19.

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Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluating the maintenance cost online that has taken into account of the failure prognostic results of performance degradation. The feasibility and effectiveness of the proposed strategy is confirmed using the NASA data set of aero-engines. Results show that the proposed strategy outperforms the two benchmark maintenance strategies: classical periodic maintenance and emerging dynamic predictive maintenance.

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Dissertationen zum Thema "Data-driven maintenance"

Sedghi, Mahdieh. "Data-driven predictive maintenance planning and scheduling." Licentiate thesis, Luleå tekniska universitet, Industriell Ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80828.

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The railway track network is one of the major modes of transportation and among a country’s most valuable infrastructure assets. Maintenance and renewal of railway infrastructure have a vital role in safety performance, the quality of the ride, train punctuality, and the life cycle cost of assets. Due to the large proportion of maintenance costs, increasing the efficiency of maintenance through optimised planning can result in high amounts of cost-saving. Moreover, from a safety perspective, late maintenance intervention can result in defective track and rollingstock components, which in severe cases, can cause severe accidents such as derailments. An effective maintenance management system is required to ensure the availability of the infrastructure system and meet the increasing capacity demand. The recent rapid technological revolution and increasing deployment of sensors and connected devices created new possibilities to increase the maintenance strategy effectiveness in the railway network. The purpose of this thesis is to expand the knowledge and methods for planning and scheduling of railway infrastructure maintenance. The research vision is to find quantitative approaches for integrated tactical planning and operational scheduling of predictive condition-based maintenance which can be put to practical use and improve the efficiency of the railway system. First, a thorough literature review study is performed to identify improvement policies for maintenance planning and scheduling in the literature and also to analyse the current approaches in optimising the maintenance planning and scheduling problem. Second, a novel data-driven multi-level decision-making framework to improve the efficiency of maintenance planning and scheduling is developed. The proposed framework aims to support the selection of track segments for maintenance by providing a practical degradation prediction model based on available condition measurement data. The framework considers the uncertainty of future predictions using the probability of surpassing a maintenance limit instead of using the predicted value. Moreover, an extensive total maintenance cost formulation is developed to include both direct and indirect preventive and corrective costs to observe the effect of using cost optimisation and grouping algorithms at the operational scheduling level. The performance of the proposed framework is evaluated through a case study based on data from a track section of the iron ore line between Boden and Luleå. The results indicate that the proposed approach can lead to cost savings in both optimal and grouping plans. This framework may be a useful decision support tool in the automated planning and scheduling of maintenance based on track geometry measurements.

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Decker, de Sousa Leticia <1981&gt. "Data-stream driven Fuzzy-granular approaches for system maintenance." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amsdottorato.unibo.it/10273/1/phdThesis_LeticiaDecker.pdf.

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Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability.

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Roychowdhury, Sayak. "Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091.

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ANTOMARIONI, SARA. "Data-driven approaches to maintenance policy definition: general framework and applications." Doctoral thesis, Università Politecnica delle Marche, 2021. http://hdl.handle.net/11566/289660.

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La competitività dell’attuale scenario industriale richiede elevati livelli di affidabilità di processo, in particolare per impianti complessi: infatti un elevato numero di componenti li rende potenzialmente più soggetti a guasti. In questo contesto, la tesi mira a proporre un framework generale per supportare il processo di gestione della manutenzione. Si presentano quattro applicazioni basate sul caso di studio di una raffineria di petrolio. Nella prima applicazione, si adotta il framework per derivare le regole di associazione tra i guasti dei componenti dopo un arresto dell'impianto di raffineria. Si identificano i componenti con maggiore probabilità di rottura entro un dato intervallo di tempo dall’arresto dell'impianto e si propone la strategia di manutenzione. La seconda applicazione si basa su un modello di ottimizzazione. Sfruttando le regole di associazione, si formula un modello di programmazione lineare intera per selezionare l'insieme ottimale di componenti da riparare per migliorare l'affidabilità dell'impianto. Nella terza applicazione, si modella un problema bi-obiettivo di riparazione dei componenti per ridurre l'impatto sia sul tempo di recupero da un arresto che sui costi complessivi di manutenzione. Questo è risolto sia attraverso l'approccio AUGMEnted ε-CONstraint sia tramite una meta-euristica Large Neighborhood Search. Nella quarta applicazione, si adottano l'Association Rule Mining (ARM) e la Social Network Analysis (SNA) per identificare le interazioni nascoste tra i componenti che portano ad un effetto domino tra i guasti. Seguendo il framework generale proposto, ARM e SNA vengono applicate anche per perseguire un secondo obiettivo: estendere l'analisi dei processi produttivi analizzando i risultati della Failure Modes Effects and Criticalities Analysis. Si considerano il caso studio di un impianto offshore e onshore per l'estrazione e lo stoccaggio di petrolio e quello di una centrale idroelettrica.<br>The competitiveness characterizing the current industrial scenario requires high levels of process reliability. This aspect is particularly relevant for complex plants since many components are potentially more subject to failure occurrence. In this context, this thesis aims to propose a general framework to support the maintenance management. Four different applications are presented, based on an oil refinery case study. In the first application, the Association Rules describing components failing after a stoppage of the oil refinery plant are mined. The components that are most likely to break within a given time interval after a plant stoppage are identified to propose the best maintenance strategy. The second application regards a predictive optimization-based maintenance policy, considering the Association Rules. An integer linear programming model is formulated to select the optimal set of components to repair to improve the plant's reliability. In the third application, a bi-objective Component Repairing Problem is developed in order to reduce the impact on both the time to recover from a stoppage and the overall maintenance costs. It is solved through the AUGMEnted ε-CONstraint approach and through a bi-objective Large Neighborhood Search meta-heuristic. In the fourth application, the Association Rule Mining (ARM) and Social Network Analysis (SNA) are contextually adopted to identify the hidden interactions between components that lead to a domino effect between failures. Following the proposed general framework, ARM and SNA are also applied to pursue a second objective: extending the analysis of the production processes in terms of failures and related effects, analyzing the results of the Failure Modes Effects and Criticalities Analysis. An offshore and onshore plant for oil and gas extraction and storing and a hydro-electrical power plant are considered as case studies.

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Okwori, Emmanuel. "Data-driven approaches for proactive maintenance planning of sewer blockage management." Licentiate thesis, Luleå tekniska universitet, Arkitektur och vatten, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83891.

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Blockages have been reported to account for a significant proportion of reported failures in sewer networks. The malfunctioning of the sewer network from blockages and the subsequent disruption to other public services and flooding may constitute a risk to the environment and human health. Due to the complex nature of underground sewer networks, a reactive approach to blockage maintenance is typically employed. However, although proactive maintenance strategies have been developed, both approaches could be expensive and highlight the need to address the problem with analytics-based methods. Although blockage triggering mechanisms may be known, sewer blockages often appear at random. Thus, it is necessary to improve the understanding of the influential mechanisms involved in forming blockages in sewer networks to support its maintenance and guarantee adequate performance levels. The overall aim of this thesis was to contribute with new knowledge, approaches and methods that can support improved proactive maintenance planning of blockages in sewer networks. Various methods to achieve the aim have been investigated in relation to asset management planning levels. At the strategic level, blockages and associated performance indicators were employed in conjunction with Poisson and partial least squares regression to assess the performance of sewer networks, including gaining additional insights. At the tactical and operational levels, a procedure was developed. The procedure combines network k-function, geographically weighted regression and random forest ensembles. The network k-function analysis explains the significance of the spatial variation of blockages. The Geographically weighted Poisson regression (GWPR) investigates the degree of influence of explanatory factors on increased blockage propensity in differentiated segments of the sewer networks. Thirdly, the random forest ensembles was used to predict pipes with blockage recurrence likelihood. A proposed conceptual framework was applied at all asset management levels to assess the state of data-driven integrated asset management (IAM), based on data quality assessments, interoperability evaluations between IAM tools, and data collection and informational benefits analysis. Results from demonstrating the methods with data from the Swedish waters statistical database and three Swedish municipal sewer networks, namely A, B and C, are presented. Blockage related performance indicators showed that the average blockage rate in medium sized networks was 2-3 times the rate in other sewer networks in Sweden. Furthermore, sewer maintenance strategies were suspected to be ineffective, and increased proactive strategies may improve maintenance efficiency. The procedure in networks A, B and C indicated that the clustering of recurrent blockages maybe linked to an increased need for flushing-related maintenance in sewer pipe networks. The degree of influence between investigated factors and increased blockage propensity indicated that these relationships were not global (not the same in all locations) within and between the sewer networks for networks A, B and C. These non-stationary relationships were observed to occur in various forms, i.e. adequate self-cleaning velocity showed positive and negative correlations in different locations. The networks with relatively more substantial spatial clusters of blockages, higher data quality and availability were observed to have a higher mean prediction accuracy. The applied conceptual framework showed that intuitive asset management characterised the current state of blockage management in the municipal sewer network C with medium to good data quality and low interoperability.

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Jiang, Tianyu. "Data-Driven Cyber Vulnerability Maintenance of Network Vulnerabilities with Markov Decision Processes." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1494203777781845.

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Larsson, Olsson Christoffer, and Erik Svensson. "Early Warning Leakage Detection for Pneumatic Systems on Heavy Duty Vehicles : Evaluating Data Driven and Model Driven Approach." Thesis, KTH, Mekatronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261207.

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Modern Heavy Duty Vehicles consist of a multitude of components and operate in various conditions. As there is value in goods transported, there is an incentive to avoid unplanned breakdowns. For this, condition based maintenance can be applied.\newline This thesis presents a study comparing the applicability of the data-driven Consensus SelfOrganizing Models (COSMO) method and the model-driven patent series introduced by Fogelstrom, applied on the air processing system for leakage detection on Scania Heavy Duty Vehicles. The comparison of the two methods is done using the Area Under Curve value given by the Receiver Operating Characteristics curves for features in order to reach a verdict.\newline For this purpose, three criteria were investigated. First, the effects of the hyper-parameters were explored to conclude a necessary vehicle fleet size and time period required for COSMO to function. The second experiment regarded whether environmental factors impact the predictability of the method, and finally the effect on the predictability for the case of nonidentical vehicles was determined.\newline The results indicate that the number of representations ought to be at least 60, rather with a larger set of vehicles in the fleet than with a larger window size, and that the vehicles should be close to identical on a component level and be in use in comparable ambient conditions.\newline In cases where the vehicle fleet is heterogeneous, a physical model of each system is preferable as this produces more stable results compared to the COSMO method.<br>Moderna tunga fordon består av ett stort antal komponenter och används i många olika miljöer. Då värdet för tunga fordon ofta består i hur mycket gods som transporteras uppstår ett incitament till att förebygga oplanerade stopp. Detta görs med fördel med hjälp av tillståndsbaserat underhåll. Denna avhandling undersöker användbarheten av den data-drivna metoden Consensus SelfOrganizing Models (COSMO) kontra en modellbaserad patentserie för att upptäcka läckage på luftsystem i tunga fordon. Metoderna ställs mot varandra med hjälp av Area Under Curve-värdet som kommer från Receiver Operating Characteristics-kurvor från beskrivande signaler. Detta gjordes genom att utvärdera tre kriterier. Dels hur hyperparametrar influerar COSMOmetoden för att avgöra en rimlig storlek på fordonsflottan, dels huruvida omgivningsförhållanden påverkar resultatet och slutligen till vilken grad metoden påverkas av att fordonsflottan inte är identisk. Slutsatsen är att COSMO-metoden med fördel kan användas sålänge antalet representationer överstiger 60 och att fordonen inom flottan är likvärdiga och har använts inom liknande omgivningsförhållanden. Om fordonsflottan är heterogen så föredras en fysisk modell av systemet då detta ger ett mer stabilt resultat jämfört med COSMO-metoden.

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Dinmohammadi, Fateme. "Data-driven risk-based modelling approaches to maintenance optimisation of railway transport assets." Thesis, Glasgow Caledonian University, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.743925.

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CIPOLLINI, FRANCESCA. "Data-Driven and Hybrid Methods for Naval Applications." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/989847.

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The goal of this PhD thesis is to study, design and develop data analysis methods for naval applications. Data analysis is improving our ways to understand complex phenomena by profitably taking advantage of the information laying behind a collection of data. In fact, by adopting algorithms coming from the world of statistics and machine learning it is possible to extract valuable information, without requiring specific domain knowledge of the system generating the data. The application of such methods to marine contexts opens new research scenarios, since typical naval problems can now be solved with higher accuracy rates with respect to more classical techniques, based on the physical equations governing the naval system. During this study, some major naval problems have been addressed adopting state-of-the-art and novel data analysis techniques: condition-based maintenance, consisting in assets monitoring, maintenance planning, and real-time anomaly detection; energy and consumption monitoring, in order to reduce vessel consumption and gas emissions; system safety for maneuvering control and collision avoidance; components design, in order to detect possible defects at design stage. A review of the state-of-the-art of data analysis and machine learning techniques together with the preliminary results of the application of such methods to the aforementioned problems show a growing interest in these research topics and that effective data-driven solutions can be applied to the naval context. Moreover, for some applications, data-driven models have been used in conjunction with domain-dependent methods, modelling physical phenomena, in order to exploit both mechanistic knowledge of the system and available measurements. These hybrid methods are proved to provide more accurate and interpretable results with respect to both the pure physical or data-driven approaches taken singularly, thus showing that in the naval context it is possible to offer new valuable methodologies by either providing novel statistical methods or improving the state-of-the-art ones.

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Lundgren, Andreas. "Data-Driven Engine Fault Classification and Severity Estimation Using Residuals and Data." Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165736.

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Recent technological advances in the automotive industry have made vehicularsystems increasingly complex in terms of both hardware and software. As thecomplexity of the systems increase, so does the complexity of efficient monitoringof these system. With increasing computational power the field of diagnosticsis becoming evermore focused on software solutions for detecting and classifyinganomalies in the supervised systems. Model-based methods utilize knowledgeabout the physical system to device nominal models of the system to detect deviations,while data-driven methods uses historical data to come to conclusionsabout the present state of the system in question. This study proposes a combinedmodel-based and data-driven diagnostic framework for fault classification,severity estimation and novelty detection. An algorithm is presented which uses a system model to generate a candidate setof residuals for the system. A subset of the residuals are then selected for eachfault using L1-regularized logistic regression. The time series training data fromthe selected residuals is labelled with fault and severity. It is then compressedusing a Gaussian parametric representation, and data from different fault modesare modelled using 1-class support vector machines. The classification of datais performed by utilizing the support vector machine description of the data inthe residual space, and the fault severity is estimated as a convex optimizationproblem of minimizing the Kullback-Leibler divergence (kld) between the newdata and training data of different fault modes and severities. The algorithm is tested with data collected from a commercial Volvo car enginein an engine test cell and the results are presented in this report. Initial testsindicate the potential of the kld for fault severity estimation and that noveltydetection performance is closely tied to the residual selection process.

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Bücher zum Thema "Data-driven maintenance"

Gama, Joao, Sepideh Pashami, Albert Bifet, et al., eds. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66770-2.

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Howell, Marvin T., and Fadi Alshakhshir. Data Driven Energy Centered Maintenance. River Publishers, 2021.

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Howell, Marvin T., and Fadi Alshakhshir. Data Driven Energy Centered Maintenance. River Publishers, 2021.

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Howell, Marvin T., and Fadi S. Alshakhshir. Data Driven Energy Centered Maintenance. Taylor & Francis Group, 2021.

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Data Driven Energy Centered Maintenance. River Publishers, 2021.

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Kiritsis, Dimitris, Melinda Hodkiewicz, Oscar Lazaro, Jay Lee, and Jun Ni, eds. Data-Driven Cognitive Manufacturing - Applications in Predictive Maintenance and Zero Defect Manufacturing. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88966-583-9.

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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning: Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-Located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. Springer International Publishing AG, 2021.

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Buchteile zum Thema "Data-driven maintenance"

Liu, Min, Ling Li, and Feng Yan. "Data-Driven Fault Diagnosis Methods." In Intelligent Predictive Maintenance. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_7.

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Alshakhshir, Fadi, and Marvin T. Howell. "Different Maintenance Types and the Need for Energy Centered Maintenance." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-2.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process – Data Collection." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-5.

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Liu, Min, Ling Li, and Feng Yan. "Data-Driven Fault Prediction Model and Methods." In Intelligent Predictive Maintenance. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_8.

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Alshakhshir, Fadi, and Marvin T. Howell. "Energy Centered Maintenance in Data Centers." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-11.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process — Measuring Equipment Current Performance." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-7.

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Alshakhshir, Fadi, and Marvin T. Howell. "Conclusion." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-16.

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Alshakhshir, Fadi, and Marvin T. Howell. "Energy Centered Maintenance to avoid Low Delta T Syndrome in Chilled Water Systems." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-10.

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Alshakhshir, Fadi, and Marvin T. Howell. "ECM Process — Updating Preventative Maintenance Plans." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-9.

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Alshakhshir, Fadi, and Marvin T. Howell. "Building Energy Centered Behavior Leading to an Energy Centered Culture." In Data Driven Energy Centered Maintenance. River Publishers, 2021. http://dx.doi.org/10.1201/9781003195108-14.

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Konferenzberichte zum Thema "Data-driven maintenance"

K, Anand, Ananthajothi K, Vinodkumar S, and N. Duraimurugan. "Machinelearnpro: Redefining Predictive Maintenance Through Data-Driven Approaches." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026495.

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Moreira, Margarida, Eliseu Pereira, and Gil Gonçalves. "Data-Driven Predictive Maintenance for Component Life-Cycle Extension." In 21st International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013014200003822.

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Li, Fengsheng, Yuwei Shang, Jinli Wang, Limei Zhou, Shuaitao Bai, and Wenke Shen. "Data-driven Condition-based Maintenance Schedules of Active Distribution Networks." In 2024 The 9th International Conference on Power and Renewable Energy (ICPRE). IEEE, 2024. https://doi.org/10.1109/icpre62586.2024.10768352.

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Nusser, Jeffrey K., Darryl J. Stimson, Eric Herzberg, and Charles A. Babish. "Data-Driven Corrosion Prevention and Control Decisions for the USAF." In SSPC 2017 Greencoat. SSPC, 2017. https://doi.org/10.5006/s2017-00041.

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Abstract Corrosion significantly impacts1 safety, availability and sustainment costs of U.S. Air Force (AF) systems and equipment. System downtime due to corrosion maintenance decreases the availability of systems to perform their National defense mission and drives the need for more aircraft and associated logistics tail. In addition, the AF spends about $5.5 Billion per year, about 21 percent of the annual AF maintenance budget, on corrosion maintenance. This cost exceeds the annual Pentagon budget for the campaign against the Islamic State.2 Because of these significant impacts, AF leaders need reliable maintenance data and analytical tools to make decisions to reduce the impact of corrosion maintenance. This paper proposes that AF maintenance leaders adopt decision-making model that is built upon metrics developed by LMI3 to prioritize opportunities for data-driven corrosion maintenance decisions. The LMI metrics methodology uses top-down and bottom-up approaches to converge on an accurate estimate for corrosion-related availability and maintenance cost. The top-down approach starts with DoD-wide data systems then uses a process of elimination to yield AF corrosion maintenance costs. The bottom-up approach aggregates labor and material cost data from maintenance records, using an algorithm or “recipe” developed jointly with AF maintenance experts, to yield availability and cost data. LMI bridges gaps between the top-down and bottom-up totals by applying statistically valid scaling factors. The resulting metrics feed a corrosion decision-making model that includes performance monitoring, corrosion problem identification, analysis of options, and selecting and launching solutions. The proposed decision-making model and metrics will enable stakeholders to make data-driven assessments of which subsystems and maintenance activities to investigate for potential corrosion maintenance improvements.

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Marangis, Demetris, Andreas Livera, George Makrides, and George E. Georgiou. "Data-driven Predictive Maintenance Alerting Routine for utility-scale Photovoltaic Systems." In 2024 3rd International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED). IEEE, 2024. https://doi.org/10.1109/synergymed62435.2024.10799487.

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Doorsamy, Wesley, and Pitshou Bokoro. "An Investigation into Unsupervised Anomaly Detection for Data-Driven Predictive Maintenance." In 2024 IEEE 12th International Conference on Intelligent Systems (IS). IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705190.

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Han, Ruoran, Xiaobing Ma, Heping Jia, and Li Yang. "Condition-Based Maintenance Scheduling Integrating Multi-Level Defect Information under Multivariate Environmental Affection." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704281.

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Muneeshwari, P., R. Suguna, G. Mary Valantina, M. Sasikala, and D. Lakshmi. "IoT-Driven Predictive Maintenance in Industrial Settings through a Data Analytics Lens." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545167.

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Lee, Hyeyoung, Sangkyun Lee, and Sungjoon Choi. "Perturbation-driven data augmentation for time series anomaly detection improvement in predictive maintenance." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825569.

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Mirkar, Sulalah, Samahit Juvekar, Arya Popat, and Heet Jain. "Smart Solar Panel Maintenance: A Data-Driven Approach Using IoT and Machine Learning." In 2025 International Conference on Sustainable Energy Technologies and Computational Intelligence (SETCOM). IEEE, 2025. https://doi.org/10.1109/setcom64758.2025.10932396.

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Berichte der Organisationen zum Thema "Data-driven maintenance"

Stefanowicz, Brian, Burkhard Wandelt, Jonas Berge, et al. Smart maintenance architecture – data-driven approach to smarter maintenance. BioPhorum, 2023. http://dx.doi.org/10.46220/2023it001.

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Light, Ethan, Shang Sai, Yanfeng Ouyang, Will O’Brien, Jesus Osorio, and Yuhui Zhai. Investigating Statewide Transit Maintenance Needs in Illinois. Illinois Center for Transportation, 2023. http://dx.doi.org/10.36501/0197-9191/23-028.

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This study’s researchers investigated transit vehicle maintenance processes, maintenance needs, and potential opportunities associated with building additional bus maintenance facilities (e.g., regional maintenance centers) in Illinois. They collected information via three main tasks. First, they conducted a literature review to document practices on preventive and corrective transit vehicle maintenance processes and explored similar or comparable projects among peer states and regions. Second, they conducted a series of interviews with Illinois local transit agencies, nonprofit organizations, Illinois administrators, and peer states to identify common challenges and opportunities with fleet maintenance as well as to capture stakeholders’ perspectives on state-sponsored maintenance service. Third, they conducted a preliminary data-driven model analysis to present a better understanding of Illinois’ needs for regional maintenance centers and to illustrate how the Illinois Department of Transportation may systematically plan regional maintenance center locations and capacities to best serve unmet demand under a range of available budget values. This study’s findings lay the foundation for more effective planning of a better network of regional maintenance centers to provide long-term benefits to IDOT and partner agencies by reducing vehicle down time, decreasing maintenance and towing costs, and allowing for greater tracking of maintenance techniques in coordination with similar agencies across Illinois.

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance human productivity, AI-driven sustainability practices for energy and resource efficiency, and predictive maintenance models that reduce downtime. Addressing ethical challenges, the Article highlights the importance of data privacy, unbiased algorithms, and the environmental responsibility of intelligent automation. Through case studies across manufacturing, healthcare, and energy sectors, readers gain insights into real-world applications of AI and ML, showcasing their impact on efficiency, quality, and safety. The Article concludes with future directions, emphasizing emerging technologies like quantum computing, human-machine synergy, and the sustainable vision for Industry 5.0, where intelligent automation not only drives innovation but also aligns with ethical and social values for a resilient industrial future. Keywords: Industry 5.0, intelligent automation, AI, machine learning, sustainability, human- machine collaboration, cobots, predictive maintenance, quality control, ethical AI, data privacy, Industry 4.0, computer vision, natural language processing, energy efficiency, adaptive logistics, environmental responsibility, industrial ecosystems, quantum computing.

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Verma, Mithlesh. Indian Municipal Finance 2023. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/imf11.2023.

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This study examines decadal time-series data from 33 Urban Local Bodies (ULBs) of varying sizes across India. The analysis reveals persistent challenges, including high dependence on Inter-Governmental Transfers (IGTs), fluctuations in tax revenue, and limited spending on Operations and Maintenance (O&M). Capital expenditures are primarily driven by program-based IGTs, prompting a reevaluation of strategies to enhance own revenue mobilization. The study emphasizes the link between revenue efforts and O&M, advocating for improved current spending to sustain existing infrastructure and support new investments

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Mahlberg, Justin, Yaguang Zhang, Sneha Jha, et al. Development of an Intelligent Snowplow Truck that Integrates Telematics Technology, Roadway Sensors, and Connected Vehicle. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317355.

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The Indiana Department of Transportation (INDOT) manages and maintains over 28,000 miles of roadways. Maintenance of the roadways includes pavement repair in the summer as well as snow removal and de-icing in the winter. The prioritization of assets during winter storm events is crucial and impacts travel and safety. The objective of this project was to identify and develop tools INDOT could provide its operators to effectively perform winter operation de-icing activities. This project examined application methods and data to provide analytics and make data-driven decisions for state-wide deployment and operations. Discovery of calibration metrics partnered with fleetwide telematics enabled the development of analytic dashboards that allowed real-time evaluations and adjustments to be made during winter operation activities. These tools will allow the agency to better treat and enhance safety for road users.

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were categorized by project size (small, medium, large, and extra-large). The critical variables were chosen after identifying their correlations, and the future values of each variable were predicted through time-series analysis. Multiple regression models using selected socio-economic variables were developed to predict the future values of pavement materials’ unit price. A case study was used to compare the results between the uniform unit prices in the current LCCA procedures and the unit prices predicted in this study. In LCCA, long-term prediction involves uncertainties due to unexpected economic trends and industrial demand and supply conditions. Economic recessions and a global pandemic are examples of unexpected events which can have a significant influence on variations in material unit prices and project costs. Nevertheless, the data-driven scientific approach as described in this research reduces risk caused by such uncertainties and enables reasonable predictions for the future. The statistical models developed to predict the future unit prices of the pavement materials through this research can be implemented to enhance the current LCCA procedure and predict more realistic unit prices and project costs for the future M&R activities, thus promoting the most cost-effective alternative in LCCA.

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Randrup, Thomas B., Agnes Pierre, Niel Sang, and Kjell Nilson. Equity in Green Space Planning and Management : synthesis study on data availability for the development of a socio-ecological index. SLU Movium Think Tank, 2025. https://doi.org/10.54612/a.7h5gdnod5n.

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As cities densify to meet environmental and economic goals, the equitable distribution of urban green spaces (UGS) becomes critical for fostering community well-being, promoting environmental justice, and enhancing climate resilience. This report presents a synthesis study conducted by the Swedish University of Agricultural Sciences (SLU) in collaboration with Nilsson Landscape, aimed at understanding the relationship between socio-economy and accessibility to UGS, to assess and enhance green equity in urban environments. The research focuses on Malmö specifically, and have involved Region Skåne as a proxy for other municipalities in southern Sweden, leveraging data on green space access, canopy cover, socio-economic indicators, and maintenance costs. The primary objective of this study was to establish a data-driven, replicable framework that quantifies green space equity at the city district level. Specifically, the research seeks to (i) identify key indicators of green space availability and socio-economic status that can be measured consistently across Swedish municipalities; (ii) develop a composite relationship (a matrix or an index) that integrates these indicators to provide actionable insights for urban planners and policymakers, and (iii) to test the applicability of this index in Malmö, illustrating its potential to guide future investments in UGS for equitable urban development. The research integrates three complementary initiatives: i. KSLA Project: A synthesis of socio-economic and green space factors relevant to equity in urban environments. ii. FoMA Project: Development and testing of green space indicators, including canopy cover, urban green space per capita, and distance to the nearest green space, in relation to socio-economic metrics like income, education, and employment. iii. Movium Partnership: Evaluation of the Green Equity Matrix, a tool that categorizes neighborhoods based on their socio-economic status (SES) and green space status (GSS), and explores policy implications and maintenance costs. The ambition to develop a matrix or an index aligns with international models such as the Tree Equity Score and Spatial Equity NYC but adapts them to the Swedish context, where socioeconomic factors and access to UGS are measured differently. Data sources include GIS-based analyses, municipal records, and socio-economic data from Statistics Sweden. All computations of UGS rely on open datasets, which are updated at varying frequencies but not always regularly. All the SES data is easily accessible and reliable, and available at DeSO level. A Green Equity Matrix was developed, including seven indicators ‘UGS per capita’, ‘canopy cover’, ‘distance to UGS’ as Green Space Status (GSS) indicators, and ‘age dependency’, ‘income’, ‘education level’, and ‘employment rate’, as Socio-Economic Status (SES) indicators. Each indicator was computed and combined into two individual indexes. All indicators are combined unweighted, meaning they are treated equally when combined. Contrary to widespread assumptions, our analysis reveals that neighbourhoods with lower SES often have higher GSS in Malmö. Lower SES neighbourhoods in Malmö were often developed around the 1960’es and early 1970’es (the Million Program), where larger parks and green spaces were prioritized. As such, we believe these areas have benefited from earlier planning efforts aimed at providing green amenities to balance socio-economic disadvantages, and that the effects of these efforts are still notable in a Swedish context like in Malmö. However, while higher GSS in lower SES areas is a positive finding, it does not necessarily reflect equitable quality or functionality of Summary green spaces. Socio-economic disparities might still influence the usability, safety, and maintenance of these green areas, affecting their actual benefits to residents. We calculated maintenance cost in DeSOs characterized by both low GSS and low SES. Here, costs range from 24 to 335 SEK per capita, with an average in Malmö being 448 SEK per capita. Even though we indicate a relationship between low SES and low maintenance cost, we recognise the need for better data, including a calculation based on actual use, rather than cost per capita. However, such data is not available today. The actual quality of UGS should be further explored and considered incorporated into or related to the matrix. This will ensure that green space interventions align with the needs and preferences of residents. In line with this, local governments’ capacities to develop such indices should be explored too. However, the use of accessible data in combination with relatively simple GIS-based socio-ecological analysis has been prioritised for this project. Thus, our proposed method does not require advanced GIS skills, making it accessible for all municipalities. The suggested method ranks neighbourhoods within a municipality or urban area, meaning the GSS and SES results cannot be directly compared across different municipalities or urban areas. However, metrics such as the percentage of neighbourhoods within each quadrant or within a certain standard deviation can still be used for comparisons with other municipalities or urban areas. Our new and nuanced understanding of the relationship between SES and GSS challenges the conventional narrative that socio-economically disadvantaged neighbourhoods lack access to green spaces. Instead, it highlights the need for context-specific urban planning and management that recognizes both the strengths and challenges of different neighbourhoods.

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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.

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Bao, Jieyi, Xiaoqiang Hu, Cheng Peng, et al. Advancing INDOT’s Friction Test Program for Seamless Coverage of System: Pavement Markings, Typical Aggregates, Color Surface Treatment, and Horizontal Curves. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317734.

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Various highway projects, roadway safety, and maintenance all hinge on pavement friction. INDOT's pavement friction test program has played a crucial role in addressing issues like wet pavement crash reduction, durable pavements surface friction, and sustainable aggregates. However, changes in the transportation sector, allied industries, societal needs, and economics present unique challenges that require proactive solutions. First, the existing field friction testing method, which uses a locked wheel skid tester (LWST) is limited to straight, flat pavement sections and excludes crash-prone areas like horizontal curves. Upgrading the program to cover horizontal curves on two-lane rural highways is vital for road safety. Second, the demand for friction testing on pavement markings at crash sites is rising. There's currently no widely accepted standard method for national-scale pavement marking friction testing. The shift to wider longitudinal pavement markings, from 4" to 6", driven by both human and autonomous vehicle safety, presents challenges for motorcyclists and pedestrians. The third challenge focuses on Color Surface Treatment (CST), which is increasingly used in Indiana bus and bike lanes for visibility, lane discipline, and friction performance, especially under frequent bus acceleration and braking. However, a lack of laboratory and field data necessitates investigating CST's metrics and requirements for adequate friction. Advancing INDOT's friction testing program to cover the entire highway system and address emerging friction challenges is imperative. The goals of this study included enhancing INDOT's friction testing, ensuring comprehensive highway network coverage and providing reliable friction data to help INDOT address safety concerns. The research encompassed a thorough evaluation of various aggregates and pavement marking materials commonly used in Indiana through laboratory experiments, field tests, and data analysis to unveil their influence on pavement friction. Field friction measurements on colored bus and bike lanes were also conducted and thoroughly analyzed. Moreover, the tire-pavement interaction on horizontal curves was assessed on airport runways and highway sections through mechanistic-empirical analysis, and a friction testing model for horizontal curves was devised using finite element analysis and machine learning methodologies.

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Morsy, Amr, and Islam Ebo. Development of Physics-Based Deterioration Models for Reinforced Soil Retaining Structures. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2024.2360.

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Reinforced soil walls are key earth retention features in the transportation infrastructure. They are used to support and retain soil in a wide variety of crucial structures, such as highways, bridges, and railways, to ensure stability. They also provide solutions for constructing embankments and slopes in constrained spaces, allowing for efficient land use and improved infrastructure planning. This study used advanced numerical modeling to improve the understanding of the behavior and long-term performance of the aging reinforced soil walls from the 1970s for asset management purposes. An asset-scale model was created to simulate the effects of weather on these walls. The model included a system to track how moisture-driven corrosion affects wall stability and performance over time. The model outputs provide implications on the wall progressive deterioration over time and estimates for the wall remaining service life. Unlike newer wall generations constructed with strict specifications that limit fill corrosivity, early wall generations may maintain high levels of moisture for prolonged periods that can significantly increase corrosion rates. Accordingly, it is recommended that fill moisture monitoring be added to asset management measures for early generation walls that could have been constructed with highly corrosive or poorly drainable fills. The results of this study indicate that even though corrosion rates vary with changes in fill moisture, the overall loss in reinforcement thickness happens at a steady rate, showing a linear relationship between cumulative corrosion and time. The results also indicate that 25% fluctuation in fill moisture has no to little effect on the cumulative corrosion, and that the average fill moisture can be used to predict an approximate long-term cumulative corrosion. Thus, it is recommended to use one year of seasonal climate data for a specific location to estimate the annual variation in fill moisture. This can predict the yearly corrosion of reinforcements, which can then be multiplied by the number of service years to estimate long-term cumulative corrosion. Finally, an asset-scale performance model based on performance-requirement failure framework was developed using the outputs of the asset-scale numerical model. These performance models can inform decisions about critical transportation infrastructure maintenance, repair, or replacement strategies, and optimizing resource allocation.

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