A renewed adversarial network for bearing fault diagnosis based on vibro-acoustic signals under speed fluctuating conditions

Large discrepancy of sample distribution resulting from speed fluctuation is a great challenge to mechanical equipment health monitoring. Existing fault diagnosis methods are often limited by the acquisition mechanism of single-modal measurement. Considering the above problems, a multidimensional features dynamically adjusted adaptive network (MFDAAN) fused vibro-acoustic modal signals is proposed in this paper. The MFDAAN considers the context information of activation features by Funnel activation (FReLU) function to activate the vibro-acoustic signal features. In order to obtain fusion features, the multidimensional features of vibro-acoustic signals are dynamically adjusted at different stages by channel attention mechanisms, which is capable of considering the global information. Wasserstein distance is employed in the domain-adversarial training strategy to improve the property extracting domain-invariant features. The effectiveness of the MFDAAN is verified by cross-domain fault diagnosis experiments in two different scenarios. The results show that the MFDAAN can achieve good diagnostic effect for the tasks set of cross-domain fault diagnosis.