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. 2024 May 29;14(1):12344.
doi: 10.1038/s41598-024-63086-9.

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Lifu Xu  1 , Soo Siang Teoh  2 , Haidi Ibrahim  1
Affiliations

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Lifu Xu et al. Sci Rep. .

Abstract

Electric motors are essential equipment widely employed in various sectors. However, factors such as prolonged operation, environmental conditions, and inadequate maintenance make electric motors prone to various failures. In this study, we propose a thermography-based motor fault detection method based on InceptionV3 model. To enhance the detection accuracy, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input images. Furthermore, we improved the performance of the InceptionV3 by integrating a Squeeze-and-Excitation (SE) channel attention mechanism. The proposed model was tested using a dataset containing 369 thermal images of an electric motor with 11 types of faults. Image augmentation was employed to increase the data size and the evaluation was conducted using fivefold cross validation. Experimental results indicate that the proposed model can achieve accuracy, precision, recall, and F1 score of 98.82%, 98.93%, 98.82%, and 98.87%, respectively. Additionally, by freezing the fully connected layers of the InceptionV3 model for feature extraction and training a Support Vector Machines (SVM) to perform classification, it is able to achieve 100% detection rate across all four evaluation metrics. This research contributes to the field of industrial motor fault diagnosis. By incorporating deep learning techniques based on InceptionV3 and SE channel attention mechanism with a traditional classifier, the proposed method can accurately classify different motor faults.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Experiment workflow diagram.
Figure 2
Figure 2
Structure of an induction motor.
Figure 3
Figure 3
Process of applying CLAHE enhancement to the dataset.
Figure 4
Figure 4
Histogram of the thermal image before and after applying CLAHE enhancement.
Figure 5
Figure 5
Structures of InceptionV3 network.
Figure 6
Figure 6
Three different initial modules in InceptionV3.
Figure 7
Figure 7
Structure of the SE block.
Figure 8
Figure 8
Structure of InceptionV3-SE.
Figure 9
Figure 9
Structure of the proposed InceptionV3-SE-SVM model.
Figure 10
Figure 10
Implementation and experiments flowchart.
Figure 11
Figure 11
The validation accuracy and loss curves during the training of the InceptionV3 and InceptionV3-SE models.
Figure 12
Figure 12
Confusion matrix for different classifiers.
Figure 13
Figure 13
Performance comparison of the proposed method and other pre-trained deep learning models.
See this image and copyright information in PMC

References

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