A rolling bearing fault diagnosis method based on multi-feature fusion of BiLSTM-DSC- transformer under strong noise background

Abstract In the backdrop of the rapid development of intelligent manufacturing technologies, bearings, as core components of mechanical equipment, play a crucial role in monitoring operational status and diagnosing faults to ensure production safety and efficiency. However, bearings often operate under severe environmental noise and frequently changing working conditions, which significantly constrain the noise resistance and generalization performance of intelligent fault diagnosis methods. Therefore, this paper proposes a multi-feature fusion method for rolling bearing fault diagnosis based on BiLSTM-DSC-Transformer (BDT). Firstly, a new nonlinear activation function, R-Swish, is designed and applied to a lightweight deep separable convolution (DSC) feature extraction module in both time and frequency domains, ensuring the model’s feature extraction capability and operational efficiency. Secondly, an adjustable BiLSTM-Transformer model is constructed, fully leveraging the advantages of Transformer in capturing long-range dependencies and the bidirectional data processing mechanism of BiLSTM to reveal the intrinsic relationships among multiple features. Finally, a feature fusion module is established to integrate multi-feature information, combined with a linear classification layer, to achieve high-accuracy fault diagnosis of bearings under complex working conditions. Experimental results from two case studies demonstrate that the proposed method exhibits excellent robustness and generalization ability under strong noise and varying load conditions, providing a new and effective technical solution for the field of bearing fault diagnosis.