A Dual-Channel Deep Learning Framework with ECA-TCN for Bearing RUL Estimation

Abstract

Predicting the Remaining Useful Life (RUL) of rolling bearings is a central problem in predictive maintenance, but most existing data-driven solutions still rely on a single vibration channel and a CNN–LSTM architecture. This combination ignores cross-axis degradation information, depends on noise-sensitive Fourier features, carries a heavy parameter count from standard convolutions, and is limited by the sequential nature of LSTM. In this paper, we propose a Dual-Channel Wavelet Scattering Network(DC-WSNet) that addresses these four limitations in a single architecture. The model uses two parallel depthwise separable CNN branches to fuse the horizontal and vertical vibration channels. It then simultaneously extracts features from the wavelet, Fourier, and time-statistical domains, recalibrates the fused channels using an Efficient Channel Attention (ECA) module, and uses a Temporal Convolutional Network (TCN) for temporal modeling. Experimental and interpretability analysis demonstrate that the proposed model effectively captures the cross-axis degradation patterns and leverages multi-axis information for improved RUL prediction.