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Unsupervised anomaly detection of machines operating under time-varying conditions: DCD-VAE enabled feature disentanglement of operating conditions and states

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  • Zhou, Haoxuan
  • Wang, Bingsen
  • Zio, Enrico
  • Lei, Zihao
  • Wen, Guangrui
  • Chen, Xuefeng

Abstract

Anomaly detection (AD) plays a key role in condition monitoring (CM) to ensure the machine system's operating reliability and safety. When machinery operates under time-varying operating conditions (TVOCs), interference from varying operating conditions (OCs) exacerbates the difficulty of AD. To address this issue, a Disentangled Representation Learning(DRL) approach is proposed to dissociate the features linked with OCs and operating states (OSs). Expanding on the pre-existing Variational Autoencoder (VAE), Distribution Constraint Decomposition (DCD) is proposed as a regularization approach, which implements a loose-tight constraint depending on Kullback-Leibler(KL) divergence to enforce prior constraints on the latent features. As a result, DCD-VAE, which enables the selective allocation of different types of information, achieving disentanglement between OCs’ information and the OSs’ information, is proposed in this paper. An anomaly indicator(ANI) constructed based on the OSs features enables AD. Simulation and experiments validate the substantial advantage of the proposed approach over comparable methods, facilitating the timely and precise identification of mechanical faults.

Suggested Citation

  • Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Lei, Zihao & Wen, Guangrui & Chen, Xuefeng, 2025. "Unsupervised anomaly detection of machines operating under time-varying conditions: DCD-VAE enabled feature disentanglement of operating conditions and states," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
  • Handle: RePEc:eee:reensy:v:256:y:2025:i:c:s0951832024007245
    DOI: 10.1016/j.ress.2024.110653
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    References listed on IDEAS

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    1. Ma, Chenyang & Li, Yongbo & Wang, Xianzhi & Cai, Zhiqiang, 2023. "Early fault diagnosis of rotating machinery based on composite zoom permutation entropy," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Zheng, Minglei & Man, Junfeng & Wang, Dian & Chen, Yanan & Li, Qianqian & Liu, Yong, 2023. "Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Chen, Junsheng & Li, Jian & Chen, Weigen & Wang, Youyuan & Jiang, Tianyan, 2020. "Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders," Renewable Energy, Elsevier, vol. 147(P1), pages 1469-1480.
    4. 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).
    5. Haoxuan Zhou & Zihao Lei & Enrico Zio & Guangrui Wen & Zimin Liu & Yu Su & Xuefeng Chen, 2023. "Conditional feature disentanglement learning for anomaly detection in machines operating under time-varying conditions," Post-Print hal-04103555, HAL.
    6. Yan, Haodong & Li, Fudong & Chen, Jinglong & Liu, Zijun & Wang, Jun & Feng, Yong & Zhang, Xinwei, 2023. "A graph embedded in graph framework with dual-sequence input for efficient anomaly detection of complex equipment under insufficient samples," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    7. 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).
    8. 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).
    9. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    10. González-Muñiz, Ana & Díaz, Ignacio & Cuadrado, Abel A. & García-Pérez, Diego, 2022. "Health indicator for machine condition monitoring built in the latent space of a deep autoencoder," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    11. 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).
    12. Jiao, Jinyang & Zhao, Ming & Lin, Jing & Liang, Kaixuan, 2019. "Hierarchical discriminating sparse coding for weak fault feature extraction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 41-54.
    13. Ding, Yifei & Jia, Minping & Zhuang, Jichao & Cao, Yudong & Zhao, Xiaoli & Lee, Chi-Guhn, 2023. "Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    14. 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).
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