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Journal articles on the topic 'Root cause diagnosis'

Author: Grafiati

Published: 24 May 2025

Last updated: 27 May 2025

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1

Seo, Eunsoo, Gulustan Dogan, Tarek Abdelzaher, and Theodore Brown. "Root cause diagnosis in error-propagating networks." Security and Communication Networks 9, no. 11 (2016): 1297–308. http://dx.doi.org/10.1002/sec.1415.

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2

Thornhill, N. F., S. L. Shah, and B. Huang. "Detection of Distributed Oscillations and Root-Cause Diagnosis." IFAC Proceedings Volumes 34, no. 27 (2001): 149–54. http://dx.doi.org/10.1016/s1474-6670(17)33583-8.

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3

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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4

A´shour, Saja W., and Theyab A. Farhan. "Identification of Diagnosis Fungi that Cause Potato Root Rot." Bionatura Journal 1 1, no. 1 (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 com

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5

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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6

Duan, Ping, Tongwen Chen, Sirish L. Shah, and Fan Yang. "Methods for root cause diagnosis of plant-wide oscillations." AIChE Journal 60, no. 6 (2014): 2019–34. http://dx.doi.org/10.1002/aic.14391.

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7

Yuan, Tao, and S. Joe Qin. "Root cause diagnosis of plant-wide oscillations using Granger causality." Journal of Process Control 24, no. 2 (2014): 450–59. http://dx.doi.org/10.1016/j.jprocont.2013.11.009.

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8

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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9

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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10

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.

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11

Jiang, Hailei, Rohit Patwardhan, and Sirish L. Shah. "Root Cause Diagnosis of Plant-wide Oscillations using the Adjacency Matrix." IFAC Proceedings Volumes 41, no. 2 (2008): 13893–900. http://dx.doi.org/10.3182/20080706-5-kr-1001.02352.

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12

Gharahbagheri, H., S. A. Imtiaz, and F. Khan. "Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network." Industrial & Engineering Chemistry Research 56, no. 8 (2017): 2054–70. http://dx.doi.org/10.1021/acs.iecr.6b01916.

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13

Rashidi, Bahador, and Qing Zhao. "Autonomous root-cause fault diagnosis using symbolic dynamic based causality analysis." Neurocomputing 401 (August 2020): 10–27. http://dx.doi.org/10.1016/j.neucom.2020.03.007.

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14

Dey, S., and J. A. Stori. "A Bayesian network approach to root cause diagnosis of process variations." International Journal of Machine Tools and Manufacture 45, no. 1 (2005): 75–91. http://dx.doi.org/10.1016/j.ijmachtools.2004.06.018.

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15

Rashidi, Bahador, Dheeraj Sharan Singh, and Qing Zhao. "Data-driven root-cause fault diagnosis for multivariate non-linear processes." Control Engineering Practice 70 (January 2018): 134–47. http://dx.doi.org/10.1016/j.conengprac.2017.09.021.

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16

Gharahbagheri, H., S. Imtiaz, F. Khan, and S. Ahmed. "Causality analysis for root cause diagnosis in Fluid Catalytic Cracking unit." IFAC-PapersOnLine 48, no. 21 (2015): 838–43. http://dx.doi.org/10.1016/j.ifacol.2015.09.631.

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17

Liu, Xiangdong, Jie Liu, Xiaohua Yang, et al. "Fault Root Cause Analysis Based on Liang–Kleeman Information Flow and Graphical Lasso." Entropy 27, no. 2 (2025): 213. https://doi.org/10.3390/e27020213.

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Root cause analysis is used to find the specific fault location and cause of a fault during system fault diagnosis. It is an important step in fault diagnosis. The root cause analysis method based on causality starts from the origin of the causal connection between transactions and infers the location and cause of the mechanism failure by analyzing the causal impact of variables between systems, which has methodological advantages. Causal analysis methods based on transfer entropy are proven to have biases in calculation results, so there is a phenomenon of calculating false causal relationshi

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18

Ouyang, Biao, Yingying Zhang, Hanyin Cheng, et al. "RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems." Proceedings of the VLDB Endowment 18, no. 4 (2024): 1169–82. https://doi.org/10.14778/3717755.3717774.

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With the continued migration of storage to cloud database systems, the impact of slow queries in such systems on services and user experience is increasing. Root-cause diagnosis plays an indispensable role in facilitating slow-query detection and revision. This paper proposes a method capable of both identifying possible root cause types for slow queries and ranking these according to their potential for accelerating slow queries. This enables prioritizing root causes with the highest impact, in turn improving slow-query revision effectiveness. To enable more accurate and detailed diagnoses, w

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19

Srinivasan, Babji, Ulaganathan Nallasivam, and Raghunathan Rengaswamy. "Diagnosis of root cause for oscillations in closed-loop chemical process systems." IFAC Proceedings Volumes 44, no. 1 (2011): 13145–50. http://dx.doi.org/10.3182/20110828-6-it-1002.01227.

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20

Choi, Minseok. "Root Cause Analysis of Electric Fires and Risk Diagnosis of Parked Vehicles." Journal of the Korean Society of Hazard Mitigation 20, no. 1 (2020): 245–50. http://dx.doi.org/10.9798/kosham.2020.20.1.245.

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Generally, a vehicle's electrical circuits are protected against overcurrent. However, an overcurrent protector cannot completely prevent fires. In particular, fires that occur in parked vehicles are often caused by Joule heat and arc faults owing to contact failures, partial disconnection, arc tracking, and connection errors in the circuit that continues to receive electricity from the battery. Since the flowing current in such a case is not an overcurrent, the fuse will not react and may thereby cause a fire. Additionally, if a wire burns and causes a short circuit after the fire, the fuse w

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21

Mdini, Maha, Gwendal Simon, Alberto Blanc, and Julien Lecoeuvre. "Introducing an Unsupervised Automated Solution for Root Cause Diagnosis in Mobile Networks." IEEE Transactions on Network and Service Management 17, no. 1 (2020): 547–61. http://dx.doi.org/10.1109/tnsm.2019.2954340.

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22

Chen, Han-Sheng, Chunhui Zhao, Zhengbing Yan, and Yuan Yao. "Root Cause Diagnosis of Oscillation-Type Plant Faults Using Nonlinear Causality Analysis." IFAC-PapersOnLine 50, no. 1 (2017): 13898–903. http://dx.doi.org/10.1016/j.ifacol.2017.08.2208.

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23

Cai, Zhengong, Wei Li, Wanyi Zhu, Lu Liu, and Bowei Yang. "A Real-Time Trace-Level Root-Cause Diagnosis System in Alibaba Datacenters." IEEE Access 7 (2019): 142692–702. http://dx.doi.org/10.1109/access.2019.2944456.

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24

Rao Mohan Rao, Harikrishna, Sirish L. Shah, and Tongwen Chen. "Application of Alarm Correlations in Root Cause Diagnosis of Plant-wide Oscillations*." IFAC-PapersOnLine 56, no. 2 (2023): 7154–59. http://dx.doi.org/10.1016/j.ifacol.2023.10.595.

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25

Staveley, Imogen C. M., and Emma S. Bointon. "Influencing organisational culture is key to successfully implementing root cause analysis to tackle late cancer diagnosis." BMJ Leader 2, no. 3 (2018): 118–23. http://dx.doi.org/10.1136/leader-2018-000067.

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IntroductionLate diagnosis of cancer increases morbidity and mortality, and the overall cost of care. Camden Clinical Commissioning Group (CCG) improved the percentage of patients diagnosed at stage I and II cancer, from 39% in 2012 to 47% in 2015;there remained a need to identify the issues leading to late diagnosis.MethodCamden CCG adapted the Royal College of General Practitioners’ cancer significant event analysis (SEA) tool to be used by general practitioner (GP) surgeries to identify the causes of delayed cancer diagnoses and to find ways to increase early diagnosis. The effective collec

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26

van Zijl, Natali, Steven Martin Bradshaw, Lidia Auret, and Tobias Muller Louw. "A Hierarchical Approach to Improve the Interpretability of Causality Maps for Plant-Wide Fault Identification." Minerals 11, no. 8 (2021): 823. http://dx.doi.org/10.3390/min11080823.

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Modern mineral processing plants utilise fault detection and diagnosis to minimise time spent under faulty conditions. However, a fault originating in one plant section (PS) can propagate throughout the entire plant, obscuring its root cause. Causality analysis identifies the cause–effect relationships between process variables and presents them in a causality map to inform root cause identification. This paper presents a novel hierarchical approach for plant-wide causality analysis that decreases the number of nodes in a causality map, improving interpretability and enabling causality analysi

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27

Silcox, Greg, and Martin Keim. "The Many Faces of Software Diagnosis." EDFA Technical Articles 9, no. 3 (2007): 6–16. http://dx.doi.org/10.31399/asm.edfa.2007-3.p006.

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Abstract Scan-logic diagnosis is used in industry for three main purposes: root-cause analysis, improving manufacturing processes, and improving designs. This article reviews the principles of scan-logic diagnosis and its applications in each of the three areas. It also discusses ongoing challenges and emerging approaches.

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28

Yang, Liu, Keping Li, Dan Zhao, Shuang Gu, and Dongyang Yan. "A Network Method for Identifying the Root Cause of High-Speed Rail Faults Based on Text Data." Energies 12, no. 10 (2019): 1908. http://dx.doi.org/10.3390/en12101908.

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Root cause identification is an important task in providing prompt assistance for diagnosis, security monitoring and guidance for specific routine maintenance measures in the field of railway transportation. However, most of the methods addressing rail faults are based on state detection, which involves structured data. Manual cause identification from railway equipment maintenance and management text records is undoubtedly a time-consuming and laborious task. To quickly obtain the root cause text from unstructured data, this paper proposes an approach for root cause factor identification by u

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29

Butool, Syeda Asiya, Nitin Kararia, Shyam Aggrawal, Rachit Mathu, Manas Bajpai, and Ankit Sharma. "Internal Root Resorption : A Review." National Journal of Health Sciences 5, no. 3 (2021): 114–18. http://dx.doi.org/10.21089/njhs.53.0114.

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Abstract: Internal resorption is an asymptomatic condition in teeth seen with past history of injury. In the crown it has a clinical observation of pink mark. It is diagnosed by chance on an x-ray examination. Primal detection and resorption treatment improves prognosis of the condition. Resorption can be broadly classified into normal physiological or pathological process associated with extensive damage to hard structures of tooth such as dentin, cementum and bone. Proper diagnosis of resorption and differentiation of resorption from internal and external resorption by radiographic presentat

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30

Porras, Carlos, Gemma Sanchez-Espin, Miguel Such, et al. "Aortitis as a Rare Cause of Aortic Aneurysm and Valve Regurgitation: Is Repair Precluded?" Case Reports in Cardiology 2018 (2018): 1–2. http://dx.doi.org/10.1155/2018/5757081.

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Aortitis is an infrequent cause of aortic root dilatation and aortic valve regurgitation. Valve-sparing procedures have been proposed, but there is not clear evidence of which is the treatment of choice. We report the case of a 38-year-old pregnant lady with a diagnosis of idiopathic aortitis associated with aortic root aneurysm and severe aortic valve regurgitation.

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31

Costello, Suzanne, and Stewart McCracken. "Perfect Edge 3D™: Enabling Root-Cause Failure Analysis." International Symposium on Microelectronics 2014, no. 1 (2014): 000647–52. http://dx.doi.org/10.4071/isom-wp23.

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This paper presents a novel technique used to prepare samples in cross-section using ion beam technology, MCS Perfect Edge™ and Perfect Edge 3D™. The resulting surfaces are deformation-free allowing high magnification and high resolution analysis of subtle nanometer scale features and material changes across a representative sample size (mm's rather than microns). This unique process can be used to prepare most material types and combinations of materials including metals, ceramics, plastics, organics, as well as samples combining hard and soft, thick and thin material layers with zero deforma

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32

Arumugam, Saravanakumar. "Failure Diagnosis and Root-Cause Analysis of In-Service and Defective Distribution Transformers." Energies 14, no. 16 (2021): 4997. http://dx.doi.org/10.3390/en14164997.

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Diagnostic data of transformers are essential in determining their integrity and to estimate their reliability. Such diagnostic data is not available for distribution transformers as only meagre attempts are made to accumulate this information. The reason for lack of such attention is attributed to the compromise made to meet financial constraints and cost-cutting measures. It is needless to emphasize the significance of these diagnostic data in evading premature failures and enhancing the accuracy of the maintenance program. Overall, the concepts of diagnostic methods are well defined regardi

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33

Kutsuna, Takuro, Shuichi Sato, and Naoya Chujo. "Efficient Root Cause Detection in Complex Embedded Systems with Abstract Model-based Diagnosis." Journal of Information Processing 20, no. 3 (2012): 767–73. http://dx.doi.org/10.2197/ipsjjip.20.767.

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34

Jiang, Hailei, Rohit Patwardhan, and Sirish L. Shah. "Root cause diagnosis of plant-wide oscillations using the concept of adjacency matrix." Journal of Process Control 19, no. 8 (2009): 1347–54. http://dx.doi.org/10.1016/j.jprocont.2009.04.013.

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35

Liu, Yi, Han-Sheng Chen, Haibin Wu, Yun Dai, Yuan Yao, and Zhengbing Yan. "Simplified Granger causality map for data-driven root cause diagnosis of process disturbances." Journal of Process Control 95 (November 2020): 45–54. http://dx.doi.org/10.1016/j.jprocont.2020.09.006.

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36

Chen, Han-Sheng, Zhengbing Yan, Yuan Yao, Tsai-Bang Huang, and Yi-Sern Wong. "Systematic Procedure for Granger-Causality-Based Root Cause Diagnosis of Chemical Process Faults." Industrial & Engineering Chemistry Research 57, no. 29 (2018): 9500–9512. http://dx.doi.org/10.1021/acs.iecr.8b00697.

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37

Mori, Junichi, Vladimir Mahalec, and Jie Yu. "Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes." Computers & Chemical Engineering 71 (December 2014): 171–209. http://dx.doi.org/10.1016/j.compchemeng.2014.07.022.

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38

Wang, Sheng, Qiang Zhao, Yinghua Han, and Jinkuan Wang. "Root cause diagnosis for process faults based on multisensor time-series causality discovery." Journal of Process Control 122 (February 2023): 27–40. http://dx.doi.org/10.1016/j.jprocont.2022.12.006.

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39

Husniah, Imraatul. "DIAGNOSIS DAN TATALAKSANA SINDROM MARFAN." JIMKI: Jurnal Ilmiah Mahasiswa Kedokteran Indonesia 8, no. 1 (2020): 71–76. http://dx.doi.org/10.53366/jimki.v8i1.40.

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ABSTRAK  Sindrom Marfan merupakan kelainan genetik jaringan ikat, bersifat autosomal dominan, dan disebabkan oleh mutasi pada gen fibrilin-1 gene (FNB1). Prevalensi sindrom Marfan yaitu 2-3 per 10.000 penduduk. Rasio perbandingan penderita antara laki-laki dan perempuan adalah sama. Sekitar 75% pasien mempunyai riwayat keluarga menderita penyakit ini. Gejala yang timbul sangat kompleks, meliputi gejala pada sistem kardiovaskular, okular, skeletal, pulmonal, dan kulit. Dilatasi root aorta merupakan penyebab utama yang meyebabkan morbiditas dan mortalitas pada penyakit sindrom Marfan. Diag

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40

Fu, Xiaowei, Yanlin Liu, and Xi Li. "Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven." Energies 13, no. 16 (2020): 4069. http://dx.doi.org/10.3390/en13164069.

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The solid oxide fuel cell (SOFC) is a new energy technology that has the advantages of low emissions and high efficiency. However, oscillation and propagation often occur during the power generation of the system, which causes system performance degradation and reduced service life. To determine the root cause of multi-loop oscillation in an SOFC system, a data-driven diagnostic method is proposed in this paper. In our method, kernel principal component analysis (KPCA) and transfer entropy were applied to the system oscillation fault location. First, based on the KPCA method and the Oscillatio

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41

Shinder, Neil, Alasdair Polson, Elizabeth Pringle, and Denis E. O'Donnell. "Neuralgic Amyotrophy: A Rare Cause of Bilateral Diaphragmatic Paralysis." Canadian Respiratory Journal 5, no. 2 (1998): 139–42. http://dx.doi.org/10.1155/1998/926375.

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Neuralgic amyotrophy, also known as brachial neuritis, is a well described clinical entity. Diaphragmatic dysfunction, as a result of phrenic nerve root involvement (cervical roots 3 to 5), is an uncommon, but increasingly recognized association. The case of a previously healthy 61-year-old woman who, after a prodrome of neck and shoulder discomfort, presented with severe orthopnea is described. Pulmonary function and electrophysiological studies led to a diagnosis of bilateral diaphragmatic paralysis. The patient's clinical course and the exclusion of other nerve entrapment syndromes and neur

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42

Yasenjiang, Jiarula, Chenxing Xu, Shengpeng Zhang, and Xin Zhang. "Fault Diagnosis and Prediction of Continuous Industrial Processes Based on Hidden Markov Model-Bayesian Network Hybrid Model." International Journal of Chemical Engineering 2022 (November 18, 2022): 1–17. http://dx.doi.org/10.1155/2022/3511073.

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Hidden Markov models (HMMs) have been recently used for fault detection and prediction in continuous industrial processes; however, the expected maximum (EM) algorithm in the HMM has local optimality problems and cannot accurately find the fault root cause variables in complex industrial processes with high-dimensional data and strong variable coupling. To alleviate this problem, a hidden Markov model-Bayesian network (HMM-BN) hybrid model is proposed to alleviate the local optimum problem in the EM algorithm and diagnose the fault root cause variable. Firstly, the model introduces expert empi

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43

e Oliveira, Eduardo, Vera L. Miguéis, and José L. Borges. "Overlap in Automatic Root Cause Analysis in Manufacturing: An Information Theory-Based Approach." Applied Sciences 13, no. 6 (2023): 3416. http://dx.doi.org/10.3390/app13063416.

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Automatic Root Cause Analysis solutions aid analysts in finding problems’ root causes by using automatic data analysis. When trying to locate the root cause of a problem in a manufacturing process, an issue-denominated overlap can occur. Overlap can impede automated diagnosis using algorithms, as the data make it impossible to discern the influence of each machine on the quality of products. This paper proposes a new measure of overlap based on an information theory concept called Positive Mutual Information. This new measure allows for a more detailed analysis. A new approach is developed for

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44

Rustagi, Tarush, Siddharth Badve, and Aseem N. Parekh. "Sciatica from a Foraminal Lumbar Root Schwannoma: Case Report and Review of Literature." Case Reports in Orthopedics 2012 (2012): 1–3. http://dx.doi.org/10.1155/2012/142143.

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Sciatica is commonly caused by lumbar prolapsed intervertebral disc (PID) and other spinal lesions. Uncommon causes like nerve root schwannoma are rarely considered in the differential diagnosis of sciatica. Spinal schwannomas occur both sporadically and in association with neurofibromatosis type 1 (NF1; von Recklinghausen's disease). This case report describes lumbar foraminal schwannoma as an unusual cause of radiculopathy, presenting clinically as a lumbar disc prolapse. The diagnosis was confirmed on MRI scan. Patient had complete symptomatic recovery following surgical enucleation of the

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45

Li, Wei, Kunze Yang, Ming Yang, Jipu Wang, and Sijuan Chen. "Intelligent Alarm Analysis for Fault Diagnosis and Management in Nuclear Power Plants." Energies 18, no. 7 (2025): 1730. https://doi.org/10.3390/en18071730.

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The safety and efficiency of nuclear power plants (NPPs) are heavily reliant on the timely and accurate decisions made by operators in the control room, particularly during fault scenarios. Traditional alarm systems often fall short in identifying the root causes of faults, especially in complex, multi-fault situations. This paper presents an advanced fault diagnosis method based on the multilevel flow model (MFM), which addresses the limitations of conventional alarm systems by linking alarms to system goals and functions. The MFM framework organizes the system hierarchically, aiding in the c

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46

Beatty, Robert M. "Persistent Nerve Root Compression by Buckling of the Longitudinal Ligament after Chemonucleolysis." Neurosurgery 20, no. 2 (1987): 332–34. http://dx.doi.org/10.1227/00006123-198702000-00025.

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Abstract Buckling of the posterior longitudinal ligament with compression against the thecal sac was the cause of persistent positional back pain after chemonucleolysis. Computed tomography in the supine and prone positions was helpful in making the diagnosis, and the cause was confirmed at laminectomy.

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47

Ramos, Alisha, Samira Sadeghi, and Hossein Tabatabaeian. "Battling Chemoresistance in Cancer: Root Causes and Strategies to Uproot Them." International Journal of Molecular Sciences 22, no. 17 (2021): 9451. http://dx.doi.org/10.3390/ijms22179451.

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With nearly 10 million deaths, cancer is the leading cause of mortality worldwide. Along with major key parameters that control cancer treatment management, such as diagnosis, resistance to the classical and new chemotherapeutic reagents continues to be a significant problem. Intrinsic or acquired chemoresistance leads to cancer recurrence in many cases that eventually causes failure in the successful treatment and death of cancer patients. Various determinants, including tumor heterogeneity and tumor microenvironment, could cause chemoresistance through a diverse range of mechanisms. In this

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48

Song, Pengyu, and Chunhui Zhao. "Sparse Adjacency Forecasting and Its Application to Efficient Root Cause Diagnosis of Process Faults." IFAC-PapersOnLine 54, no. 3 (2021): 439–44. http://dx.doi.org/10.1016/j.ifacol.2021.08.281.

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49

Wang, Jian-Guo, Rui Chen, Jing-Ru Su, et al. "Root cause diagnosis of plant-wide oscillations based on fuzzy kernel multivariate Granger causality." Journal of the Taiwan Institute of Chemical Engineers 149 (August 2023): 104928. http://dx.doi.org/10.1016/j.jtice.2023.104928.

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50

Dong, Jie, Ruiqi Sun, Kaixiang Peng, Zhijie Shi, and Liang Ma. "Quality Monitoring and Root Cause Diagnosis for Industrial Processes Based on Lasso-SAE-CCA." IEEE Access 7 (2019): 90230–42. http://dx.doi.org/10.1109/access.2019.2926067.

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