A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings. (October 2021)

• Record Type: Journal Article
• Authors: Zhao, Bingxi; Yuan, Qi
• Is Part Of: Journal of manufacturing systems. Volume 61(2021)
• Page Start: 450
• Page End: 460
• Publication Date: 2021-10
• Subjects: Bearing degradation -- Deep convolutional neural network -- Online correction -- Remaining useful lifetime; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.10.004
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A novel scheme involving both classification and regression is proposed for the online RUL prediction of bearings. The CNN-based method is employed for online monitoring of a bearing's health state. Raw signals are directly mapped with the RUL, thus eliminating the experience-based health indicator construction. Adaptive delay correction is proposed for online correction of the prediction results. Abstract: The remaining useful life (RUL) prediction of a rolling element bearing is important for more reasonable maintenance of machinery and equipment. Generally, the information of a failure can hardly be acquired in advance while running and the degradation process varies in terms of different faults. Thus, fault identification is indispensable for a multi-condition RUL prediction, where, however, the fault identification and RUL prediction are separated in most studies. A new hybrid scheme is proposed in this paper for the multi-condition RUL prediction of rolling element bearings. The proposed scheme contains both classification and regression, where the 2D-DCNN based classifier and predictors are built concerning typical fault conditions of a bearing. For the online prediction, the raw signals are spanned in the time-frequency domain and then transferred into images as the input of the scheme. The classifier is used to monitor the vibration of rolling bearings for online fault recognition and excite the corresponding predictor for RUL prediction once a fault is detected. The output from the predictor is amended by the proposed adaptive delay correction method as the final prediction results. A demonstration is performed based on the XJTU-SY datasets and the results are compared with those from the state-of-the-art methods, which proves the superiority of the proposed scheme in improving the accuracy and linearity of RUL prediction. The time cost of the proposed online prediction scheme is also investigated and the results indicate high time effectiveness.