Multi-condition fault diagnosis method for rotating machinery based on whale optimization variational mode decomposition algorithm and deep residual network

Aiming at the problem that the difference in fault data distribution caused by the variation of gear and bearing conditions during the operation of rotating machinery affects the accuracy of fault diagnosis, a multi-condition fault diagnosis approach based on whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep residual shrinkage network (DRSN) is proposed. Firstly, WOA is employed to optimize the parameters of the decomposition level and the penalty factor value in VMD, enhancing its noise resistance. Subsequently, VMD processing yields several modal components, each characterized by a permutation entropy feature vector, ensuring optimal feature extraction. DRSN is then utilized, employing an attention mechanism to adaptively set soft threshold functions, thereby improving gear fault diagnosis accuracy under multiple operating conditions. Finally, the proposed method’s performance was validated through the collection of gear running signals under various conditions, and the fault diagnosis accuracy was used as the model evaluation index to conduct comparative experiments and ablation experiments. The experimental results indicate that the WOA-VMD feature extraction and DRSN fault diagnosis techniques outperform traditional methods in multi-condition gear fault diagnosis. This study provides a new effective approach for multi-condition diagnosis of rotating machinery.