滚动轴承的退化特征信息融合与剩余寿命预测

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中图分类号：TH182

DOI：10.3969/j.issn.1004-132X.2025.07.018 开放科学（资源服务）标识码（OSID）：

Fusion of Degradation Feature Information and Remaining Life Prediction for Rolling Bearings

ZHANG Jianyu* WANG Liuzhen XIAO Yong MA Yanan Beijing Key Laboratory of Advanced Manufacturing Technology，Beijing University of Technology， Beijing，100124

Abstract：To address the demands for remaining life prediction of roling bearings，a prediction model was proposed based on SAE and BiLSTM network. Taking the full-life vibration data of rolling bearings as research object，a degradation index set was constructed by developing a hyperbolic inverse transformation-based health indicator and a frequency-domain harmonic degradation factor. The SAE was employed for feature fusion to extract key features and eliminate redundant information. Meanwhile，the BiLSTM model was utilized to capture temporal dependencies and achieve full-cycle life prediction. Experimental results demonstrate that the proposed model outperforms support vector regression，extreme learning machines，and convolutional neural networks in terms of smallr prediction errors and stronger generalization capabilities.

Key words： sparse autoencoder（SAE） feature fusion； bidirectional long short-term memory（BiL-STM） network predictive model； rolling bearing； inverse hyperbolic characteristic index； frequencydomain harmonic degradation factor

0 引言

滚动轴承是机械装备的重要支撑部件，其性能对设备的稳定运行至关重要。（剩余12615字）