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

被引：0

作者：

Zhang H. ^{[1
]}

Wang Q. ^{[1
]}

Chen L. ^{[1
]}

Zhou J. ^{[1
]}

Shao H. ^{[2
]}

机构：

[1] School of Electrical Information Engineering, Jiangsu University of Technology, Changzhou

[2] Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, 89154, NV

来源：

Energy Engineering: Journal of the Association of Energy Engineering | 2023年 / 120卷 / 08期

基金：

中国国家自然科学基金;

关键词：

deep learning; fault diagnosis; Induction motors; thermal images;

D O I：

10.32604/ee.2023.028453

中图分类号：

学科分类号：

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. © 2023, Tech Science Press. All rights reserved.

引用

页码：1867 / 1883

页数：16

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