Partial Transfer Learning of Multidiscriminator Deep Weighted Adversarial Network in Cross-Machine Fault Diagnosis

Abstract

Deep transfer learning provides a feasible fault diagnosis method for intelligent mechanical systems. However, this method usually assumes that the source domain and the target domain have the same label space, which greatly limits its application in the actual industry. Therefore, this article proposes a multidiscriminator deep weighted adversarial network (MDWAN) method, which is especially suitable for partial transfer learning where the number of target domain categories is less than the source domain. The proposed method is mainly composed of three parts: feature extractor, multidiscriminator, and classifier. In the feature extraction section, a deep separable convolutional neural network model (DSC) is proposed, which can greatly reduce parameter calculation amount under the premise of ensuring the extraction accuracy. In the multidiscriminator section, a multidiscriminator weighted learning strategy is proposed. This strategy comprehensively considers the domain information and source domain label information, and introduces a weight function to quantify the contribution of source domain samples to the domain discriminator and classifier, which can effectively identify and filter outlier source samples to promote the positive transfer of shared samples. In order to verify the effectiveness and feasibility of the proposed method, the method is applied to three types of bearing datasets of different machines. Comparing the classification results of different methods, the conclusion shows that this method is more beneficial for bearing fault classification.