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Learning the health index of complex systems using dynamic conditional variational autoencoders

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  • Wei, Yupeng
  • Wu, Dazhong
  • Terpenny, Janis

Abstract

Recent advances in sensing technologies have enabled engineers to collect big data to predict the remaining useful life (RUL) of complex systems. Current modeling techniques for RUL predictions are usually not able to quantify the degradation behavior of a complex system through a health index. Although some studies have been conducted to learn the health index of degradation systems, most of the existing methods are highly dependent on pre-defined assumptions which may not be consistent with the real degradation behaviors. To address this issue, we introduce a time-dependent directed graphical model to characterize the probabilistic relationships among sensor signals, RUL, operational conditions, and health index. Based on the graphical model, a dynamic conditional variational autoencoder is proposed to learn the health index. The experimental results have shown that the proposed method can learn an effective and reliable health index that measures complex system degradation behavior. Moreover, the learned health index improves the accuracy of RUL predictions.

Suggested Citation

  • Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2021. "Learning the health index of complex systems using dynamic conditional variational autoencoders," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021005147
    DOI: 10.1016/j.ress.2021.108004
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Li, Tianmei & Pei, Hong & Si, Xiaosheng & Lei, Yaguo, 2023. "Prognosis for stochastic degrading systems with massive data: A data-model interactive perspective," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Lin, Yan-Hui & Chang, Liang & Guan, Lu-Xin, 2024. "Enhanced stochastic recurrent hybrid model for RUL Predictions via Semi-supervised learning," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. Coraça, Eduardo M. & Ferreira, Janito V. & Nóbrega, Eurípedes G.O., 2023. "An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2024. "Remaining useful life prediction using graph convolutional attention networks with temporal convolution-aware nested residual connections," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Kumar, Anil & Parkash, Chander & Vashishtha, Govind & Tang, Hesheng & Kundu, Pradeep & Xiang, Jiawei, 2022. "State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    6. Chao, Qun & Shao, Yuechen & Liu, Chengliang & Yang, Xiaoxue, 2023. "Health evaluation of axial piston pumps based on density weighted support vector data description," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Zhou, Zhihao & Zhang, Wei & Yao, Peng & Long, Zhenhua & Bai, Mingling & Liu, Jinfu & Yu, Daren, 2024. "More realistic degradation trend prediction for gas turbine based on factor analysis and multiple penalty mechanism loss function," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    9. Wang, Yueyao & Lee, I-Chen & Hong, Yili & Deng, Xinwei, 2022. "Building degradation index with variable selection for multivariate sensory data," Reliability Engineering and System Safety, Elsevier, vol. 227(C).

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