Bearing Remaining Useful Life Prediction Based on a Scaled Health Indicator and a LSTM Model with Attention Mechanism

被引：20

作者：

Gao, Songhao ^{[1
]}

Xiong, Xin ^{[1
,2
]}

Zhou, Yanfei ^{[1
]}

Zhang, Jiashuo ^{[1
]}

机构：

[1] Shanghai Univ, Sch Mechatron & Automat Engn, Shanghai 200444, Peoples R China

[2] Shanghai Key Lab Intelligent Mfg & Robot, Shanghai 200444, Peoples R China

来源：

MACHINES | 2021年 / 9卷 / 10期

基金：

中国国家自然科学基金;

关键词：

remaining useful life (RUL); rolling bearing; health indicator; phase space warping; long and short-term memory (LSTM); FAULT-DIAGNOSIS; SIGNATURE;

D O I：

10.3390/machines9100238

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Rotor systems are of considerable importance in most modern industrial machinery, and the evaluation of the working conditions and longevity of their core component-the rolling bearing-has gained considerable research interest. In this study, a scale-normalized bearing health indicator based on the improved phase space warping (PSW) and hidden Markov model regression was established. This indicator was then used as the input for the encoder-decoder LSTM neural network with an attention mechanism to predict the rolling bearing RUL. Experiments show that compared with traditional health indicators such as kurtosis and root mean square (RMS), this scale-normalized bearing health indicator directly indicates the actual damage degree of the bearing, thereby enabling the LSTM model to predict RUL of the bearing more accurately.

引用

页数：26

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