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Broad zero-shot diagnosis for rotating machinery with untrained compound faults

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  • Ma, Chenyang
  • Wang, Xianzhi
  • Li, Yongbo
  • Cai, Zhiqiang

Abstract

Compound fault diagnosis of rotating machinery is of great significance for the operational reliability and security of manufacturing equipment. Since the possible compound fault types increase exponentially, the compound faults appear in the test phase may not be covered during training, posing great challenge for machine health monitoring. Recently, several methods attempt to construct the semantic space for untrained compound faults. However, the semantic space suffers from the low-fidelity to identify the untrained compound faults. Besides, most of these methods only focus on untrained compound faults, ignoring more common single faults in the test set. To address these issues, a novel broad zero-shot diagnosis method (BZSD) is proposed to identify both single faults and untrained compound faults. Firstly, the multiresolution permutation entropy is presented to identify single faults and preliminarily screen out the untrained compound faults, which can prevent the compound fault from being biased towards the trained single fault. Then, a high-fidelity semantic space is constructed to classify the pre-screened compound faults. The proposed fault semantics are close to the ground truth semantics, which is conducive to improving diagnostic accuracy. The experiments demonstrate the effectiveness and superiority of the BZSD for rotating machinery with untrained compound faults.

Suggested Citation

  • Ma, Chenyang & Wang, Xianzhi & Li, Yongbo & Cai, Zhiqiang, 2024. "Broad zero-shot diagnosis for rotating machinery with untrained compound faults," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s095183202300618x
    DOI: 10.1016/j.ress.2023.109704
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    References listed on IDEAS

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    1. Jiao, Ruihua & Peng, Kaixiang & Dong, Jie & Zhang, Chuanfang, 2020. "Fault monitoring and remaining useful life prediction framework for multiple fault modes in prognostics," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    2. Zhao, Chao & Shen, Weiming, 2022. "Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Yiping Gao & Liang Gao & Xinyu Li & Yuwei Zheng, 2020. "A zero-shot learning method for fault diagnosis under unknown working loads," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 899-909, April.
    5. Chen, Xu & Zhao, Chunhui & Ding, Jinliang, 2023. "Pyramid-type zero-shot learning model with multi-granularity hierarchical attributes for industrial fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    6. Guo, Jianchun & Si, Zetian & Liu, Yi & Li, Jiahao & Li, Yanting & Xiang, Jiawei, 2022. "Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    7. Liu, Jie & Xu, Huoyao & Peng, Xiangyu & Wang, Junlang & He, Chaoming, 2023. "Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Yan, Shen & Shao, Haidong & Min, Zhishan & Peng, Jiangji & Cai, Baoping & Liu, Bin, 2023. "FGDAE: A new machinery anomaly detection method towards complex operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    9. Shi, Peiming & Wu, Shuping & Xu, Xuefang & Zhang, Bofei & Liang, Pengfei & Qiao, Zijian, 2023. "TSN: A novel intelligent fault diagnosis method for bearing with small samples under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    10. Zhao, Chao & Shen, Weiming, 2022. "Dual adversarial network for cross-domain open set fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    11. Bai, Rui & Noman, Khandaker & Feng, Ke & Peng, Zhike & Li, Yongbo, 2023. "A two-phase-based deep neural network for simultaneous health monitoring and prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    12. Wang, Zhenya & Yao, Ligang & Cai, Yongwu & Zhang, Jun, 2020. "Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis," Renewable Energy, Elsevier, vol. 155(C), pages 1312-1327.
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