Direct Remaining Useful Life Prediction for Rolling Bearing Using Temporal Convolutional Networks

Author(s):  
Chongdang Liu ◽  
Linxuan Zhang ◽  
Cheng Wu
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ying Zhang ◽  
Anchen Wang

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value.


2020 ◽  
Vol 32 (2) ◽  
pp. 024006
Author(s):  
Jian Tang ◽  
Guanhui Zheng ◽  
Dong He ◽  
Xiaoxi Ding ◽  
Wenbin Huang ◽  
...  

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401988977 ◽  
Author(s):  
Yanfeng Peng ◽  
Yanfei Liu ◽  
Junsheng Cheng ◽  
Yu Yang ◽  
Kuanfang He ◽  
...  

There are two difficulties in the remaining useful life prediction of rolling bearings. First, the vibration signals are always interfered by noise signals. Second, some of the extracted features include useless information which may decrease the prediction accuracy. In order to solve the problems above, corresponding methods are employed in this article. First, adaptive sparsest narrow-band decomposition is utilized for extracting the degradation information from noise. Compared with the commonly used empirical mode decomposition method, problems including mode mixture and boundary effect caused by the calculation of extremas is not required. Second, locality-preserving projection is applied for merging the meaningful information from the original data and reduces the dimension of features. Based on adaptive sparsest narrow-band decomposition and locality preserving projection, a novel approach is employed for the remaining useful life prediction. The prediction procedure is as follows. First, the signals are analyzed by adaptive sparsest narrow-band decomposition and the feature vectors are constructed. Afterwards, the features are fused by locality preserving projection to merge useful information from the features. Least squares support vector machine is applied for the remaining useful life prediction in the end. The analysis results indicate that the proposed approach is reliable for rolling bearing remaining useful life prediction.


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