Publication: Motor fault classification using thermal imaging and modified inceptionv3 model
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Date
2024-12-01
Authors
Xu, Lifu
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Abstract
Industrial motors are considered one of the essential equipment widely used in various sectors. However, due to factors such as prolonged operation, environmental conditions, and inadequate maintenance, industrial motors are prone to various failures. This study proposes a thermography-based motor fault detection method utilizing the InceptionV3 model, addressing its limitations in handling noise, low-contrast images, and small datasets through several enhancements. To overcome the problem of low contrast in thermal images, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to the input images. Furthermore, the Squeeze-and-Excitation (SE) channel attention mechanism is integrated into the InceptionV3 model to improve its performance. The proposed model was tested using a publicly available dataset containing 369 thermal images of an electric motor with 11 types of faults. Experiment results show that an accuracy of 98.13% is achieved by the model. The trained InceptionV3-SE model was used for feature extraction to train a Support Vector Machine (SVM) classifier, which attained a maximum accuracy of 100%. Achieving perfect accuracy in this context highlights the proposed method's ability to overcome challenges such as class imbalance and blurred inter-class boundaries, setting a new benchmark for motor fault detection systems. This research contributes to the field of industrial motor fault classification. The effectiveness of the proposed method in accurately identifying various motor faults is demonstrated, which holds significant value for real-world industrial applications.