A Novel Rotating Component Comprehensive Diagnostic Network Based on Stacked Auto Encoders and Sparse Filter Combined with Generative Adversarial Network

Nowadays, the majority of intelligent fault diagnosis approaches are still centered on individual faulty components, while only a limited number of models are capable of performing integrated diagnosis for rotating systems that consist of shafts, bearings, and gears. Under variable-speed operating conditions, the large scale of vibration data further complicates the process of effective feature extraction. To improve these challenges, this study develops a comprehensive diagnostic framework for rotating components, termed WGAN-SAFC. The proposed architecture integrates a Wasserstein Generative Adversarial Network (WGAN) with a hybrid structure of stacked autoencoders and sparse filtering (SAFC). SAFC integrates the feature-learning capability of SAE and the sparsity-driven representation of SF, while incorporating adversarial data generation to address sample imbalance and enhance fault diagnosis performance. Experimental verification on collected vibration datasets demonstrates that WGAN-SAFC achieves superior diagnostic accuracy and robustness compared with existing methods.