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Adaptive model of gate recursive element feature fusion domain for predicting bearing life

Published:2024-04-24 author:ZENG Yuhai, CHENG Feng, WEI Chunhu, et al. Browse: 233 Check PDF documents

Adaptive model of gate recursive element feature fusion domain for

predicting bearing life

ZENG Yuhai1, CHENG Feng1, WEI Chunhu1, YANG Shifei2

(1.School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China;

2.Nanjing Chaos Data Technology Co., Ltd., Nanjing 210012, China)

Abstract: Aiming at the problem of the decrease accuracy of existing data-driven models in predicting the remaining using life (RUL) of bearings under different operating conditions,a bearing prediction method based on gated recursive unit feature fusion domain adaptive (GFFDA) model was proposed. Firstly, the signal analysis method was used to extract features from the bearing vibration signal, and the feature evaluation method was used to select 5 optimal features. Based on the optimal features, the support vector machine optimized by particle swarm optimization was used to partition the health stages of the bearing. Then, the optimal feature subsets of the degradation stage in the target domain and source domain were selected as inputs for the GFFDA model, and the feature extractor and lifespan prediction module were pre-trained using source domain data. Finally, the target feature extractor and lifespan prediction module were updated to predict the RUL of the target domain. The proposed method was validated using the bearing dataset from Xi'an Jiaotong University. The research results indicate that the GFFDA model has better cross condition analysis ability and excellent information extraction ability compared to existing data-driven models, and has better performance in bearing life prediction tasks under different working conditions.

Key words: rolling bearings; remaining using life(RUL); feature evaluation; adversarial adaptation; gated recursive unit feature fusion domain adaptive(GFFDA) model; data-driven model

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