Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches  

作　　者：Hong Zhang Qi Wang Lixing Chen Jiaming Zhou Haijian Shao 

机构地区：[1]School of Electrical Information Engineering,Jiangsu University of Technology,Changzhou,213001,China [2]Department of Electrical and Computer Engineering,University of Nevada,Las Vegas,NV 89154,USA

出　　处：《Energy Engineering》2023年第8期1867-1883,共17页能源工程（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62001197);National High Technology Research and Development Program(863 Program)(2011AA05A107);Natural Sciences Research Grant for Colleges and Universities of Jiangsu Province(No.22KJD470002);Jiangsu Provincial Postgraduate Research and Practice Innovation Program(No.XSJCX21_58).

摘　　要：Induction motors(IMs)typically fail due to the rate of stator short-circuits.Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-destructive fault detection is universally perceived as a difficult issue.This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs.Based on the publicly available thermal images(IRT)dataset related to condition monitoring of electrical equipment-IMs,the proposed approach outperforms the highest training accuracy,validation accuracy,and testing accuracy,i.e.,99%,100%,and 94%,respectively,compared with 8 benchmark approaches based on deep learning models and 3 existing approaches in the literature for 11-class IMs faults.Even the training loss,validation loss,and testing loss of the eleven deployed deep learning models meet industry standards.

关 键 词：Induction motors fault diagnosis thermal images deep learning 

分 类 号：TM3[电气工程—电机]