Search for Author, Title, Keyword
RESEARCH PAPER
Temporal Constrained Dynamic Uncertain Causality Graph for Root Cause Analysis of Intermittent Faults
,
 
,
 
,
 
,
 
 
 
More details
Hide details
1
the School of Computer Science, Northwestern Polytechnical University, China
 
2
the Unmanned System Research Institute, Northwestern Polytechnical University, China
 
3
the School of Cybersecurity, Northwestern Polytechnical University, China
 
 
Submission date: 2024-04-25
 
 
Final revision date: 2024-06-22
 
 
Acceptance date: 2024-08-08
 
 
Online publication date: 2024-08-29
 
 
Publication date: 2024-08-29
 
 
Corresponding author
Yangming Guo   

the School of Cybersecurity, Northwestern Polytechnical University, China
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(1):192169
 
HIGHLIGHTS
  • A better understanding of the intermittency of fault symptoms from the perspective of temporal and state coupling between variables.
  • A novel causal model, TC-DUCG, designed foranalyzing intermittent faults caused by temporal and state coupling during fault propagation.
  • A fault diagnosis inference method that takes into account the dynamic evolution of faults and the random uncertainty of the propagation process.
KEYWORDS
TOPICS
ABSTRACT
The diagnosis of intermittent faults is crucial in the field of maintenance support. However, most existing studies focus on the analysis of intermittent faults of single components, ignoring the more complex intermittent failures of equipment functions due to the coupling of multivariate anomalous states in the fault propagation process. Existing diagnostic methods based on fault propagation models, which mainly focus on one-dimensional temporal or logical relationships, fall short in representing and reasoning about intermittent faults caused by temporal and state coupling. In this paper, we propose a Temporal Constrained Dynamic Uncertain Causality Graph (TC-DUCG) model to fill this gap and effectively model intermittent faults. The model not only considers the probability of fault propagation among variables, but also integrates temporal constraints. It also presents a diagnostic reasoning process to investigate potential causes of intermittent faults. We provide an illustrative example to demonstrate the effectiveness of the proposed method in diagnosing intermittent faults.
ACKNOWLEDGEMENTS
This work was supported partially by the National Natural Science Foundation of China under grant No.62203361, the National Fund under grant No.0622-GKGJ30000030094-ZB-Z002-0, the Young Talent Fund of Association for Science and Technology in Shaanxi, the Project of National Defense Basic Research Program, National Key Scientific Research Project under grant No.MJZ2-4N21.
REFERENCES (29)
1.
Bakhshi R, Kunche S, Pecht M. Intermittent failures in hardware and software. Journal of Electronic Packaging. 2014;136(1). https://doi.org/10.1115/1.4026....
 
2.
Qi H, Ganesan S, Pecht M. No-fault-found and intermittent failures in electronic products. Microelectronics Reliability. 2008;48(5):663-74. https://doi.org/10.1016/j.micr....
 
3.
Shen Q, Qiu J, Liu G, Lv K. Intermittent fault’s parameter framework and stochastic petri net based formalization model. Eksploatacja i Niezawodnosc - Maintenance and Reliability. 2016;18(2):210-7. https://doi.org/10.17531/ein.2....
 
4.
Yuan S, Wang H, Sun X. Research on intermittent fault diagnosis of rolling bearing based on interval-valued evidence construction and possibility. Measurement. 2022;203: https://doi.org/10.1016/j.meas....
 
5.
Lin L, Zhou S, Hsieh SY. Neural Network Enabled Intermittent Fault Diagnosis Under Comparison Model. IEEE Transactions on Reliability. 2023;72(3):1206-19. https://doi.org/10.1109/TR.202....
 
6.
Qu J, Fang X, Chai Y, Tang Q, Liu J. An intermittent fault diagnosis method of analog circuits based on variational modal decomposition and adaptive dynamic density peak clustering. Soft Computing. 2022;26(17):8603-15. https://doi.org/10.1007/s00500....
 
7.
Fang X, Qu J, Chai Y, Liu B. Adaptive multiscale and dual subnet convolutional auto-encoder for intermittent fault detection of analog circuits in noise environment. ISA Transactions. 2023;136:428-41. https://doi.org/10.1016/j.isat....
 
8.
Fang X, Qu J, Chai Y. Self-supervised intermittent fault detection for analog circuits guided by prior knowledge. Reliability Engineering & System Safety. 2023;233: https://doi.org/10.1016/j.ress....
 
9.
Wang S, Liu Z, Jia Z, Zhao W, Li Z. Intermittent fault diagnosis for electronics-rich analog circuit systems based on multi-scale enhanced convolution transformer network with novel token fusion strategy. Expert Systems with Applications. 2024;238: https://doi.org/10.1016/j.eswa....
 
10.
Zhou D, Zhao Y, Wang Z, He X, Gao M. Review on diagnosis techniques for intermittent faults in dynamic systems. IEEE Transactions on Industrial Electronics. 2019;67(3):2337-47. https://doi.org/10.1109/TIE.20....
 
11.
Jin Y, Zhang Q, Chen Y, Lu Z, Zu T. Cascading failures modeling of electronic circuits with degradation using impedance network. Reliability Engineering & System Safety. 2023;233: https://doi.org/10.1016/j.ress....
 
12.
Cui Y, Shi J, Wang Z. Fault propagation reasoning and diagnosis for computer networks using cyclic temporal constraint network model. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2016;47(8):1965-78. https://doi.org/10.1109/TSMC.2....
 
13.
Xu J, Wang R, Liang Z, Liu P, Gao J, Wang Z. Physics-guided, data-refined fault root cause tracing framework for complex electromechanical system. Reliability Engineering & System Safety. 2023;236: https://doi.org/10.1016/j.ress....
 
14.
Li W, Li H, Gu S, Chen T. Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities. Control Engineering Practice. 2020;105: https://doi.org/10.1016/j.cone....
 
15.
Zhu P, Han J, Liu L, Zuo MJ. A stochastic approach for the analysis of fault trees with priority and gates. IEEE Transactions on Reliability. 2014;63(2):480-94. https://doi.org/10.1109/TR.201....
 
16.
Zhu P, Han J, Liu L, Lombardi F. A stochastic approach for the analysis of dynamic fault trees with spare gates under probabilistic common cause failures. IEEE Transactions on Reliability. 2015;64(3):878-92. https://doi.org/10.1109/TR.201....
 
17.
Cai B, Huang L, Xie M. Bayesian networks in fault diagnosis. IEEE Transactions on Industrial Informatics. 2017;13(5):2227-40. https://doi.org/10.1109/TII.20....
 
18.
Kumari P, Bhadriraju B, Wang Q, Kwon JSI. A modified Bayesian network to handle cyclic loops in root cause diagnosis of process faults in the chemical process industry. Journal of Process Control. 2022;110:84-98. https://doi.org/10.1016/j.jpro....
 
19.
Huang W, Kou X, Zhang Y, Mi R, Yin D, Xiao W, et al. Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach. Reliability Engineering & System Safety. 2021;205: https://doi.org/10.1016/j.ress....
 
20.
Zhang Q. Dynamic uncertain causality graph for knowledge representation and reasoning: Discrete DAG cases. Journal of Computer Science and Technology. 2012;27(1):1-23. https://doi.org/10.1007/s11390....
 
21.
Zhang Q, Geng S. Dynamic uncertain causality graph applied to dynamic fault diagnoses of large and complex systems. IEEE Transactions on Reliability. 2015;64(3):910-27. https://doi.org/10.1109/TR.201....
 
22.
Li L, Yue W. Dynamic uncertain causality graph based on Intuitionistic fuzzy sets and its application to root cause analysis. Applied Intelligence. 2020;50(1):241-55. https://doi.org/10.1007/s10489....
 
23.
Dong C, Zhou J. A New Algorithm of Cubic Dynamic Uncertain Causality Graph for Speeding Up Temporal Causality Inference in Fault Diagnosis. IEEE Transactions on Reliability. 2023;72(2):662-77. https://doi.org/10.1109/TR.202....
 
24.
Zhang Q, Bu X, Zhang M, Zhang Z, Hu J. Dynamic uncertain causality graph for computer-aided general clinical diagnoses with nasal obstruction as an illustration. Artificial Intelligence Review. 2021;54:27-61. https://doi.org/10.1007/s10462....
 
25.
Abdelwahed S, Karsai G, Mahadevan N, Ofsthun SC. Practical implementation of diagnosis systems using timed failure propagation graph models. IEEE Transactions on Instrumentation and Measurement. 2008;58(2):240-7. https://doi.org/10.1109/TIM.20....
 
26.
Breitfelder K, Messina D. IEEE 100: the authoritative dictionary of IEEE standards terms. Standards Information Network IEEE Press v879. 2000.
 
27.
Zhong T, Qu J, Fang X, Li H, Wang Z. The intermittent fault diagnosis of analog circuits based on EEMD-DBN. Neurocomputing. 2021;436:74-91. https://doi.org/10.1016/j.neuc....
 
28.
Dong C, Zhang Q. The cubic dynamic uncertain causality graph: A methodology for temporal process modeling and diagnostic logic inference. IEEE Transactions on Neural Networks and Learning Systems. 2020;31(10):4239-53. https://doi.org/10.1109/TNNLS.....
 
29.
Li Q. Analysis and Modeling Method of Hierarchical Propagation Characteristics of Electronic Equipment Faults. National University of Defense Technology; 2021.
 
eISSN:2956-3860
ISSN:1507-2711
Journals System - logo
Scroll to top