RUL prediction method based on cross-view hybrid network model

Remaining Useful Life (RUL) prediction has become a core technology in the field of prognostics and health management (PHM). However, due to the non-stationarity, weak signal characteristics and concurrent multiple faults of original signals, the estimation of RUL in a single view tends to ignore the structural relationship of samples in different spaces. To this end, this paper designs a RUL prediction framework based on a cross-view hybrid network model (CVHNet). Firstly, a dual-channel feature extraction hybrid network (DCF-HybridNet) is constructed. The original features are decomposed into time-frequency features and implicit spatial features through short-time Fourier transform (STFT) and Gramian angular difference field (GADF) - CNN, and then fused into a comprehensive feature representation with cross information. Secondly, a RUL regression algorithm integrating Transformer encoder and nonlinear fitter is developed to automatically learn the correlation between features in different views and predict RUL. The XJTU-SY rolling bearing dataset was used as an example for experimental verification. The results show that the Average CRA of the CVHNet method proposed in this paper is 0.9195 under three different working conditions. Compared with the methods in the same field, it shows superior performance and can provide strong support for the predictive maintenance of equipment.