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A deep learning based health index construction method with contrastive learning

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  • Wang, Hongfei
  • Li, Xiang
  • Zhang, Zhuo
  • Deng, Xinyang
  • Jiang, Wen

Abstract

Health index (HI) can help equipment maintenance personnel better understand the health status of equipment. However, how to construct a HI generation model with robust predictive performance and strong interference-resistant ability is still a pressing problem to be solved. This paper presents a new HI construction method that combines unsupervised learning with contrastive learning. In the proposed method, a multi-granularity contrastive learning module is designed to extract in-depth feature from the data. This module operates at both the instance and subsequence levels, ensuring comprehensive feature extraction, and its introduction enhances the interference resistance of the HI generation model. Furthermore, this approach exclusively utilizes the monotonicity of the HI to design the target loss function, ensuring that the model maintains excellent predictive performance across various scenarios. To address the issues that may arise when constructing an unsupervised HI generation model solely based on monotonicity, such as unclear trends and periodic monotonicity, this paper innovatively introduces a localization loss function to tackle these problems. The effectiveness of the proposed HI generation method are evaluated by assessing the performance of the generated HI in remaining useful life (RUL) prediction. The experimental results indicate that this method exhibits robust predictive performance across various scenarios.

Suggested Citation

  • Wang, Hongfei & Li, Xiang & Zhang, Zhuo & Deng, Xinyang & Jiang, Wen, 2024. "A deep learning based health index construction method with contrastive learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023007135
    DOI: 10.1016/j.ress.2023.109799
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    References listed on IDEAS

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    1. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    3. Changyue Song & Kaibo Liu, 2018. "Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach," IISE Transactions, Taylor & Francis Journals, vol. 50(10), pages 853-867, October.
    4. Zhou, Taotao & Zhang, Xiaoge & Droguett, Enrique Lopez & Mosleh, Ali, 2023. "A generic physics-informed neural network-based framework for reliability assessment of multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
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    1. 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).

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