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RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network

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  • Li, Jimeng
  • Mao, Weilin
  • Yang, Bixin
  • Meng, Zong
  • Tong, Kai
  • Yu, Shancheng

Abstract

Rolling bearings are widely used in mechanical equipment, effectively determining the failure time of rolling bearings is particularly significant to ensure the safe performance of mechanical equipment. However, in industrial scenarios, the machine mainly works in the normal state for a long time, it is difficult to accumulate the same distribution of the whole life data, but the use of different distribution of data for forecasting will reduce the performance of deep learning-based prediction methods. Therefore, in order to tackle this problem, a multi-scale convolutional parallel memory domain adaptation network is investigated to forecast the residual useful life (RUL) of bearings across working conditions. Firstly, a new characteristic extractor—multi-scale convolutional parallel memory network is designed to extract spatial and temporal characteristics of bearing degradation data. At the same time, in order to minimize the distribution difference between source domain and target domain, a temporal-spatial feature alignment strategy is proposed to obtain domain invariable characteristics by combining maximum mean difference and domain adversarial learning. Finally, the availability of the proposed approach is verified using two rolling bearing data sets. The results reveal that it can efficiently forecast the RUL of rolling bearings across working conditions.

Suggested Citation

  • Li, Jimeng & Mao, Weilin & Yang, Bixin & Meng, Zong & Tong, Kai & Yu, Shancheng, 2024. "RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007688
    DOI: 10.1016/j.ress.2023.109854
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    References listed on IDEAS

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    1. Ding, Yifei & Zhuang, Jichao & Ding, Peng & Jia, Minping, 2022. "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
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    5. Zhou, Taotao & Zhang, Laibin & Han, Te & Droguett, Enrique Lopez & Mosleh, Ali & Chan, Felix T.S., 2023. "An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
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    7. Ding, Wanmeng & Li, Jimeng & Mao, Weilin & Meng, Zong & Shen, Zhongjie, 2023. "Rolling bearing remaining useful life prediction based on dilated causal convolutional DenseNet and an exponential model," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
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    Cited by:

    1. 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).

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