An Adversarial Multisource Data Subdomain Adaptation Model: A Promising Tool for Fault Diagnosis of Induction Motor Under Cross-Operating Conditions

Abstract

Typical domain adaptation neural network that takes multi-source heterogeneous data as input usually achieves poor diagnostic accuracy in induction motor fault diagnosis under cross-operating conditions. Aiming at this problem, the present study proposes an adversarial multi-source data subdomain adaptation (AMDSA) model. This model encapsulates three types of modules: a shared feature extractor, a label predictor and a series of domain discriminators. The joint operation of the shared feature extractor and the domain discriminators is used to perform subdomain adaptation of different types of data for obtaining domain invariant features of multi-source heterogeneous data. The label predictor is employed to fuse these domain invariant features and realize label classification. The proposed model can solve the problem of multi domain adaptation in multi-source heterogeneous data through constructing a subdomain adaptation strategy and a feature fusion strategy. The effectiveness of AMDSA is verified by a series of diagnostic experiments on faulty induction motors under cross-operating conditions. The experimental results show that the average diagnostic accuracy of all cross-operating conditions reaches 97.62%.