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A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions

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  • Miao, Mengqi
  • Yu, Jianbo
  • Zhao, Zhihong

Abstract

As a key component in the machinery, the health of bearings directly affects working performance of machinery. Recently, many data-driven methods have been proposed to predict remaining useful life (RUL) of rolling bearings. However, most methods neglected the problem of data distribution difference caused by different operation conditions, which will lead to prediction performance deteriorating greatly on other bearings. To solve the domain shift problem in bearing RUL prediction, a sparse domain adaption network (SDAN) is proposed in this study. Firstly, an adaptive selection mechanism is proposed to select important input features in SDAN. Besides, a novel feature extractor, adaptively convolutional neural network (ACNN) is proposed to capture essential information from the selected features by adjusting receptive fields adaptively. The sparse feature selection layer is developed to suppress noise and remove ineffective features based on the noise filtering of sparse representation. Besides, the sparse domain adaption is used in SDAN by integrating domain-adversarial leaning and unsupervised sparse domain alignment to solve the problem of data distribution shift. Finally, the effectiveness of SDAN is verified on the PRONOSTIA rolling bearing dataset. The results demonstrate that SDAN can extract essential features and provide transferable RUL prediction performance under different working conditions.

Suggested Citation

  • Miao, Mengqi & Yu, Jianbo & Zhao, Zhihong, 2022. "A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s0951832021007353
    DOI: 10.1016/j.ress.2021.108259
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    References listed on IDEAS

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    1. Chen, Chuanhai & Li, Bowen & Guo, Jinyan & Liu, Zhifeng & Qi, Baobao & Hua, Chunlei, 2022. "Bearing life prediction method based on the improved FIDES reliability model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    2. Chen, Pengfei & Zhao, Rongzhen & He, Tianjing & Wei, Kongyuan & Yuan, Jianhui, 2023. "A novel bearing fault diagnosis method based joint attention adversarial domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Xu, Dan & Xiao, Xiaoqi & Liu, Jie & Sui, Shaobo, 2023. "Spatio-temporal degradation modeling and remaining useful life prediction under multiple operating conditions based on attention mechanism and deep learning," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Yan, Jianhai & Ye, Zhi-Sheng & He, Shuguang & He, Zhen, 2024. "A feature disentanglement and unsupervised domain adaptation of remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Guo, Junchao & He, Qingbo & Zhen, Dong & Gu, Fengshou & Ball, Andrew D., 2023. "Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Dong, Shaojiang & Xiao, Jiafeng & Hu, Xiaolin & Fang, Nengwei & Liu, Lanhui & Yao, Jinbao, 2023. "Deep transfer learning based on Bi-LSTM and attention for remaining useful life prediction of rolling bearing," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    7. Yang, Jing & Wang, Xiaomin, 2024. "Meta-learning with deep flow kernel network for few shot cross-domain remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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