A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing |
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Affiliation: | 1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China;2. Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu, 611731, China;3. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, 510275, China;4. Guangdong Provincial Key Laboratory of Fire Science and Technology, Guangzhou, 510006, China;1. School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;2. Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;3. State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China |
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Abstract: | In this study, a novel deep convolutional neural network-bootstrap-based integrated prognostic approach for the remaining useful life (RUL) prediction of rolling bearing is developed. The proposed architecture includes two main parts: 1) a deep convolutional neural network–multilayer perceptron (i.e., DCNN–MLP) dual network is utilized to simultaneously extract informative representations hidden in both time series-based and image-based features and to predict the RUL of bearings, and 2) the proposed dual network is embedded into the bootstrap-based implementation framework to quantify the RUL prediction interval. Unlike other deep-learning-based prognostic approaches, the proposed DCNN-bootstrap integrated method has two innovative features: 1) both 1D time series-based and 2D image-based features of bearings, which can multi-dimensionally characterize the degradation of bearings, are comprehensively leveraged by the proposed dual network, and 2) the RUL prediction interval can be effectively quantified without relying on the bearing’s physical or statistical prior information based on bootstrap implementation paradigm. The proposed approach is experimentally validated with two case studies on rolling element bearings, and comparisons with other state-of-the-art techniques are also presented. Subsequently, our code will be open sourced. |
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Keywords: | Bearings Deep learning Bootstrap Remaining useful life prediction Deep convolutional neural network Prognostic and health management |
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