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Abstract:
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%.
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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN: 0018-9456
Year: 2023
Volume: 72
5 . 6
JCR@2023
5 . 6 0 0
JCR@2023
ESI Discipline: ENGINEERING;
ESI HC Threshold:35
JCR Journal Grade:1
CAS Journal Grade:2
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