TY  - EJOU
AU  - Zhang, Hong 
AU  - Wang, Qi 
AU  - Chen, Lixing 
AU  - Zhou, Jiaming 
AU  - Shao, Haijian 

TI  - Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches
T2  - Energy Engineering

PY  - 2023
VL  - 120
IS  - 8
SN  - 1546-0118

AB  - 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.
KW  - Induction motors; fault diagnosis; thermal images; deep learning

DO  - 10.32604/ee.2023.028453