Research on Compound Fault Diagnosis of Bearings Using an Improved DRSN-GRU Dual-Channel Model

In practical engineering, noise often contaminates the fault signals of rolling bearings, making it difficult to accurately diagnose compound faults. To tackle this challenge, this article introduces a rolling bearing compound fault diagnosis model using an enhanced dual-channel deep residual shrinking network (DRSN)-GRU structure. The model improves the soft threshold function of the residual shrinkage building unit (RSBU), creating the progressive RSBU (PRSBU) module. It constructs a DRSN channel for initial feature extraction, while the gated recurrent unit (GRU) is integrated with convolutional pooling layers to form the GRU channel, designed for extracting linear features. By using a dual-channel connection approach, the model minimizes potential information loss or error accumulation that can occur in a single model structure. In the recognition module, a multilabel classification framework is established to identify compound faults. Experimental results show that, under strong noise conditions, the improved DRSN-GRU significantly outperforms the standard DRSN-GRU and other models, achieving 91.2% accuracy while effectively decoupling and recognizing compound faults.