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A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network

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  • Melani, Arthur Henrique de Andrade
  • Michalski, Miguel Angelo de Carvalho
  • da Silva, Renan Favarão
  • de Souza, Gilberto Francisco Martha

Abstract

Through Condition-Based Maintenance strategy, planners can monitor the health of the machinery and recommend actions based on the information obtained. Nevertheless, this approach depends on the successful establishment of Fault Detection and Diagnosis (FDD) processes. Although FDD is a research area in full growth with the development of several methods and heuristics, the availability of data from systems under a fault condition is still scarce in many applications, mainly related to complex systems. In many circumstances, only data from the system in healthy conditions is available and the applied FDD method should be able to detect variations in system conditions and diagnose faults without the need for previous labeled fault data. In this context, this article proposes a hybrid framework to automate FDD based on Moving Window Principal Component Analysis (MWPCA) and Bayesian Network (BN). First, the knowledge base on technical systems is organized to support the next steps of the framework. Then, the detection and diagnosis processes are performed sequentially through MWPCA and BN. The framework was implemented in the analysis of a simplified model of a hydrogenerator, considering real and simulated data. The results showed that the proposed method was able to detect and diagnose several simulated failures.

Suggested Citation

  • Melani, Arthur Henrique de Andrade & Michalski, Miguel Angelo de Carvalho & da Silva, Renan Favarão & de Souza, Gilberto Francisco Martha, 2021. "A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003574
    DOI: 10.1016/j.ress.2021.107837
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    1. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    2. Yunpeng Fan & Wei Zhang & Yingwei Zhang, 2014. "Monitoring of Nonlinear Time-Delay Processes Based on Adaptive Method and Moving Window," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, July.
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    5. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Xu, Jinjin & Wang, Rongxi & Liang, Zeming & Liu, Pengpeng & Gao, Jianmin & Wang, Zhen, 2023. "Physics-guided, data-refined fault root cause tracing framework for complex electromechanical system," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    8. Sahu, Atma Ram & Palei, Sanjay Kumar, 2022. "Fault analysis of dragline subsystem using Bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Dually attentive multiscale networks for health state recognition of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Shao, Kaixuan & He, Yigang & Xing, Zhikai & Du, Bolun, 2023. "Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    12. Fang, Xiaoyu & Qu, Jianfeng & Chai, Yi, 2023. "Self-supervised intermittent fault detection for analog circuits guided by prior knowledge," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    13. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).

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