Fault diagnosis of switched reluctance motor based on deep feature fusion of multi-channel current signals
This paper proposes a new deep learning framework One Dimensional Unified transformer(1D-Uniformer)based on the integration of convolution neural networks(CNN)with Transformer to address the classification and identification of high resistance connection faults and interphase short circuit faults in switched reluctance motors.First,an experimental platform for fault diagnosis of switched reluctance motors is built to set up high resistance connection faults and interphase short circuit faults on the stator windings of switched reluctance motors,and the three-phase current signals of the motors are collected by a non-intrusive method.Then,the dynamic position embedding,the multicollinear relationship aggregator,and feed-forward networks are introduced to improve the traditional CNN,and the 1D-Uniformer is obtained to sufficiently extract the features of the high resistance connection faults and interphase short circuit faults.Our experimental results indicate the model achieves impressive classification in both high resistance connection faults and interphase short circuit faults diagnosis and reaches an accuracy of 100%in the recognition of 18 types of faults.Additionally,it achieves an excellent robustness under different noise conditions.
switched reluctance motorhigh resistance connection faultsinterphase short circuit faultsCNN and transformers1D-Uniformer
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