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

Typical domain adaptation neural network that takes multisource 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 multisource 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 multisource 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 multidomain adaptation in multisource 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%.