Prediction method for bearing residual life based on a FCVAE network

被引：0
|
作者：
Zhang J. [1 ]
Zou Y. [1 ]
Jiang Y. [1 ]
Zeng D. [1 ]
机构：
[1] School of Mechanical Engineering, Southwest Jiaotong University, Chengdu
来源：
Zou, Yisheng | 1600年 / Chinese Vibration Engineering Society卷 / 39期
关键词：
Bearings; Feature extraction; Fully convolutional variational auto-encoder (FCVAE); Residual life prediction;
D O I：
10.13465/j.cnki.jvs.2020.19.003
中图分类号：
学科分类号：
摘要：
Due to influences of manufacturing technology and working condition, service lives of the same type bearings often have great individual difference. If the generalization ability of features extracted in signals is insufficient, the stability of bearing residual life prediction results is poorer. Here, a feature extraction method based on a fully convolutional variational auto-encoder (FCVAE) network was proposed for bearing residual life prediction. In this method, the fully convolutional neural network (FCNN) was used to improve the variational auto-encoder (VAE), reduce the network complexity and strengthen the generalization ability of features extracted. Frequency domain signals were taken as model input to further reduce difficulty of feature learning. At the same time, a weighted average method was designed to smooth the predicted results. Multi-condition test data were used to verify the effectiveness of the proposed method. Results showed that compared with traditional support vector regression (SVR), the average error of prediction results with the proposed method reduces 64%; compared to the convolutional neural network (CNN) and VAE, it reduces 45.5% and 47.5%, respectively. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：13 / 18and25
页数：1812
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