A Method of Bearing Remaining Useful Life Estimation Based on Convolutional Long Short-term Memory Neural Network

被引：0
|
作者：
Wang J. [1 ,2 ]
Yang S. [2 ]
Liu Y. [2 ]
Wen G. [1 ]
机构：
[1] State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha
[2] State Key Laboratory of Mechanical Behavior in Traffic Engineering Structure and System Safety, Shijiazhuang Tiedao University, Shijiazhuang
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2021年 / 57卷 / 21期
关键词：
ConvLSTM; Deep learning; Health indicator; Rolling bearing; RUL;
D O I：
10.3901/JME.2021.21.088
中图分类号：
学科分类号：
摘要：
Traditional prediction methods of bearing remaining useful life estimation based on data driven method still need some prior knowledge, such as feature index selection, health index construction, failure threshold selection and so on. In order to overcome this shortcoming, a new method for bearing residual life prediction based on deep learning method is proposed. The core of this method includes health index construction and remaining useful life calculation. First proposed a without prior knowledge of health indicators generated neural network based on ConvLSTM, the network combines the local feature extraction ability of convolutional neural network and long length of time dependent characteristics of long short-term memory neural network. This network can directly mining the characteristics of degradation degree from the original signal and build health indicators to realize the original high-dimensional data to lower dimensional feature mapping. And it uses the Sigmoid function to unify the threshold to [0, 1] interval, realized the unification of the threshold value; Then, the particle filter is used to update the double exponential life model to realize the output of the remaining life results. The method is verified by bearing life test and compared with other related methods. The results show that the health index constructed by this method has better trend, monotonicity and robustness, and the accuracy of remaining useful life prediction is higher. © 2021 Journal of Mechanical Engineering.
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页码：88 / 95
页数：7
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