Mechanical fault diagnosis by using dynamic transfer adversarial learning

Different machine learning approaches have been developed for the fault diagnosis of mechanical systems. To achieve desired diagnosis performance, lots of labeled one-dimensional (1D) signals are required for training machine learning models. however, those signals collected under various working conditions are difficult to be used for both diagnosis model training and testing. For real applications, moreover, the collection of labeled data is more difficult than that of unlabeled ones. To tackle the above challenging points, a dynamic transfer adversarial learning (DTAL) network is proposed for dealing with unsupervised fault diagnosis missions. To this end, an improved feature extractor is developed to deal with 1D mechanical vibration signals. A dynamic adversarial factor is presented to automatically adapt the marginal distribution of the global domain. The conditional distribution of the local domain is employed to make the model independent of training multiple classifiers, so as to reduce the computational burden of the proposed method. The addressed DTAL was evaluated using fault diagnosis experiments for a wind turbine gearbox and benchmark bearings. Compared with other state-of-the-art methods, it has better accuracy and robustness as highlighted by experimental results. The developed model can improve the diagnosis performance under various workloads for mechanical systems.