A novel spatio-temporal characteristic extraction network for bearing remaining useful life prediction

Abstract

Abstract Remaining useful life (RUL) is an important index indicating the health status of equipment, which has attracted extensive attention. Nevertheless, many existing RUL prediction methods encounter difficulties in effectively capturing comprehensive degradation features hidden in the data. Moreover, within real-world industrial scenarios, noisy signals are inevitably collected in the raw signals, thereby posing a big challenge to the precision of RUL predictions. To address the aforementioned problems, a robust RUL estimation approach based on degradation intrinsic mode functions (IMFs) selection and spatio-temporal feature regression is developed in this paper. The former addresses the issue of deep learning models struggling to extract degradation features of rolling bearings due to interference factors in vibration signals, while the latter resolves the problem of incomplete degradation features extracted by traditional RUL models under complex operating conditions. Firstly, complete ensemble empirical mode decomposition with adaptive noise is adopted to process the raw signals, separating components with degradation features, ineffective components, and noise. Subsequently, an IMFs selection method employing fast dynamic time warping and cosine coefficients is designed to obtain the valuable degradation features. Finally, a spatio-temporal feature extraction network is presented to comprehensively and effectively capture both spatial and temporal features within the chosen degradation IMFs, achieving the prediction of RUL with high accuracy and strong robustness. The experimental part containing two case studies has validated the effectiveness and superiority of the proposed method.