@Article{ee.2023.028453,
AUTHOR = {Hong Zhang, Qi Wang, Lixing Chen, Jiaming Zhou, Haijian Shao},
TITLE = {Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches},
JOURNAL = {Energy Engineering},
VOLUME = {120},
YEAR = {2023},
NUMBER = {8},
PAGES = {1867--1883},
URL = {http://www.techscience.com/energy/v120n8/52971},
ISSN = {1546-0118},
ABSTRACT = {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.},
DOI = {10.32604/ee.2023.028453}
}