Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

被引:10
|
作者
Tian, Qiaoping [1 ]
Wang, Honglei [1 ,2 ]
机构
[1] Guizhou Univ, Sch Management, Guiyang 550025, Peoples R China
[2] Key Lab Internet Collaborat Intelligent Mfg Guizh, Guiyang 550025, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 11期
基金
中国国家自然科学基金;
关键词
smart manufacturing; remaining useful life prediction; reliability; features compression and computing; quantile regression; FAULT-DIAGNOSIS; NEURAL-NETWORKS; SELECTION; DENSITY; MODEL;
D O I
10.3390/app11114773
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.
引用
收藏
页数:27
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