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Sensitivity analysis by differential importance measure for unsupervised fault diagnostics

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  • Floreale, Giovanni
  • Baraldi, Piero
  • Lu, Xuefei
  • Rossetti, Paolo
  • Zio, Enrico

Abstract

Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique.

Suggested Citation

  • Floreale, Giovanni & Baraldi, Piero & Lu, Xuefei & Rossetti, Paolo & Zio, Enrico, 2024. "Sensitivity analysis by differential importance measure for unsupervised fault diagnostics," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007603
    DOI: 10.1016/j.ress.2023.109846
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    1. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Peter Emerson, 2013. "The original Borda count and partial voting," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 40(2), pages 353-358, February.
    3. Do, Phuc & Bérenguer, Christophe, 2020. "Conditional reliability-based importance measures," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Zhou, Taotao & Han, Te & Droguett, Enrique Lopez, 2022. "Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    6. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    8. Zhou, Taotao & Zhang, Laibin & Han, Te & Droguett, Enrique Lopez & Mosleh, Ali & Chan, Felix T.S., 2023. "An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. Fang, Yi-Ping & Zio, Enrico, 2013. "Unsupervised spectral clustering for hierarchical modelling and criticality analysis of complex networks," Reliability Engineering and System Safety, Elsevier, vol. 116(C), pages 64-74.
    10. Lu, Xuefei & Baraldi, Piero & Zio, Enrico, 2020. "A data-driven framework for identifying important components in complex systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    11. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2022. "A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    12. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    13. Xiaoyan Zhu & Way Kuo, 2014. "Importance measures in reliability and mathematical programming," Annals of Operations Research, Springer, vol. 212(1), pages 241-267, January.
    14. Zhu, Xiaoyan & Chen, Zhiqiang & Borgonovo, Emanuele, 2021. "Remaining-useful-lifetime and system-remaining-profit based importance measures for decisions on preventive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    16. Sobol’, I.M. & Kucherenko, S., 2009. "Derivative based global sensitivity measures and their link with global sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3009-3017.
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