IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v228y2022ics0951832022003775.html
   My bibliography  Save this article

A general enhancement method for test strategy generation for the sequential fault diagnosis of complex systems

Author

Listed:
  • Wang, Jingyuan
  • Liu, Zhen
  • Wang, Jiahong
  • Long, Bing
  • Zhou, Xiuyun

Abstract

In order to improve the reliability, operational readiness and system safety of equipment, testability should be seriously considered in the design stage. As an important part of design for testability, test sequence generation is a binary identification problem because a minimal expected cost testing procedure must be developed in order to determine the amount of possible failure sources, if any, are present. Many algorithms have been proposed, but the generation time is long or the test cost is high when dealing with a large-scale dependency matrix. To address this issue, we propose a general enhancement method based on the SVM, the ECA* and the Monte Carlo. It can be applied to any existing algorithm and can effectively improve the performance. The available tests are classed based on the SVM according to the information of nodes, the ECA* is used to cluster states, and the morphological function of the test sequence is obtained through the Monte Carlo simulation. All this information is fused to dynamically adjust the scale of the dependency matrix and selected to modify the parameters. Experiments show that the existing algorithms have shorter calculation time and lower costs because the information is considered more comprehensively after enhancement.

Suggested Citation

  • Wang, Jingyuan & Liu, Zhen & Wang, Jiahong & Long, Bing & Zhou, Xiuyun, 2022. "A general enhancement method for test strategy generation for the sequential fault diagnosis of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:reensy:v:228:y:2022:i:c:s0951832022003775
    DOI: 10.1016/j.ress.2022.108754
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022003775
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108754?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tian, Heng & Duan, Fuhai & Fan, Liang & Sang, Yong, 2019. "Novel solution for sequential fault diagnosis based on a growing algorithm," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    2. Lee, Seunggyu, 2021. "Monte Carlo simulation using support vector machine and kernel density for failure probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    3. Zhang, Aibo & Wu, Shengnan & Fan, Dongming & Xie, Min & Cai, Baoping & Liu, Yiliu, 2022. "Adaptive testing policy for multi-state systems with application to the degrading final elements in safety-instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Cai, Wei & Zhao, Jingyi & Zhu, Ming, 2020. "A real time methodology of cluster-system theory-based reliability estimation using k-means clustering," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    5. Wang, Chuan & Liu, Yupeng & Wang, Dongbo & Wang, Guorong & Wang, Dingya & Yu, Chao, 2021. "Reliability evaluation method based on dynamic fault diagnosis results: A case study of a seabed mud lifting system," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    6. Mobin, Mohammadsadegh & Li, Zhaojun & Cheraghi, S. Hossein & Wu, Gongyu, 2019. "An approach for design Verification and Validation planning and optimization for new product reliability improvement," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    7. Cui, Yiqian & Shi, Junyou & Wang, Zili, 2015. "An analytical model of electronic fault diagnosis on extension of the dependency theory," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 192-202.
    8. Shi, Junyou & He, Qingjie & Wang, Zili, 2020. "Integrated Stateflow-based simulation modelling and testability evaluation for electronic built-in-test (BIT) systems," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    9. Sen Deng & Bo Jing & Hongliang Zhou, 2017. "Heuristic particle swarm optimization approach for test point selection with imperfect test," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 37-50, January.
    10. Sohn, Se Do & Hyun Seong, Poong, 2006. "Testing digital safety system software with a testability measure based on a software fault tree," Reliability Engineering and System Safety, Elsevier, vol. 91(1), pages 44-52.
    11. Zhang, Aibo & Srivastav, Himanshu & Barros, Anne & Liu, Yiliu, 2021. "Study of testing and maintenance strategies for redundant final elements in SIS with imperfect detection of degraded state," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    12. Rocco S., Claudio M. & Zio, Enrico, 2007. "A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 593-600.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaofeng Lv & Deyun Zhou & Yongchuan Tang & Ling Ma, 2018. "An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    2. Tian, Heng & Duan, Fuhai & Fan, Liang & Sang, Yong, 2019. "Novel solution for sequential fault diagnosis based on a growing algorithm," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    3. Zhou, Siwei & Ye, Luyao & Xiong, Shengwu & Xiang, Jianwen, 2022. "Reliability analysis of dynamic fault trees with Priority-AND gates based on irrelevance coverage model," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Chunyan, Ling & Jingzhe, Lei & Way, Kuo, 2022. "Bayesian support vector machine for optimal reliability design of modular systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Mohammad Javad Rahimdel & Behzad Ghodrati, 2021. "Risk Prioritization for Failure Modes in Mining Railcars," Sustainability, MDPI, vol. 13(11), pages 1-14, May.
    6. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    7. Ota, Shuhei & Kimura, Mitsuhiro, 2017. "A statistical dependent failure detection method for n-component parallel systems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 376-382.
    8. Krzysztof Gaska & Agnieszka Generowicz & Anna Gronba-Chyła & Józef Ciuła & Iwona Wiewiórska & Paweł Kwaśnicki & Marcin Mala & Krzysztof Chyła, 2023. "Artificial Intelligence Methods for Analysis and Optimization of CHP Cogeneration Units Based on Landfill Biogas as a Progress in Improving Energy Efficiency and Limiting Climate Change," Energies, MDPI, vol. 16(15), pages 1-19, July.
    9. Wang, Tianzhe & Chen, Zequan & Li, Guofa & He, Jialong & Liu, Chao & Du, Xuejiao, 2024. "A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Wu, Jiawei & Wan, Liangqi, 2024. "Reliability sensitivity analysis for RBSMC: A high-efficiency multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Chiwoo Park & Jianhua Z. Huang & Yu Ding, 2010. "A Computable Plug-In Estimator of Minimum Volume Sets for Novelty Detection," Operations Research, INFORMS, vol. 58(5), pages 1469-1480, October.
    12. Bakeer, Tammam, 2023. "General partial safety factor theory for the assessment of the reliability of nonlinear structural systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    13. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    14. Yang, Chunzhen & Liu, Jingquan & Zeng, Yuyun & Xie, Guangyao, 2019. "Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model," Renewable Energy, Elsevier, vol. 133(C), pages 433-441.
    15. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    16. Van Huynh, Thu & Tangaramvong, Sawekchai & Do, Bach & Gao, Wei & Limkatanyu, Suchart, 2023. "Sequential most probable point update combining Gaussian process and comprehensive learning PSO for structural reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    17. Li, Xin & Yang, Yu & Wu, Zhantao & Yan, Ke & Shao, Haidong & Cheng, Junsheng, 2022. "High-accuracy gearbox health state recognition based on graph sparse random vector functional link network," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    18. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    19. Zhang, Xinwei & Feng, Yong & Chen, Jinglong & Liu, Zijun & Wang, Jun & Huang, Hong, 2024. "Knowledge distillation-optimized two-stage anomaly detection for liquid rocket engine with missing multimodal data," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    20. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:228:y:2022:i:c:s0951832022003775. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.