重庆理工大学学报2024，Vol.38Issue(13)：211-219,9.DOI:10.3969/j.issn.1674-8425(z).2024.07.027
多通道电流信号深度特征融合的开关磁阻电机故障诊断研究
Fault diagnosis of switched reluctance motor based on deep feature fusion of multi-channel current signals
摘要
Abstract
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.关键词
开关磁阻电机/高阻接触故障/相间短路故障/CNN与Transformer/1D-UniformerKey words
switched reluctance motor/high resistance connection faults/interphase short circuit faults/CNN and transformers/1D-Uniformer分类
信息技术与安全科学引用本文复制引用
郭浩,宋俊材,陆思良..多通道电流信号深度特征融合的开关磁阻电机故障诊断研究[J].重庆理工大学学报,2024,38(13):211-219,9.基金项目
国家自然科学基金项目(52375522) （52375522）
