A novel deep convolutional neural network-bootstrap integrated method for RUL prediction of rolling bearing

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.

Automated summary

This study introduces a novel deep convolutional neural network-bootstrap integrated prognostic approach for predicting the remaining useful life (RUL) of rolling bearings. The method combines 1D time series and 2D image feature representations for comprehensive degradation characterization and quantifies the RUL prediction interval effectively.