Deep Learning-Based Explainable Fault Diagnosis Model With an Individually Grouped 1-D Convolution for Three-Axis Vibration Signals

This article proposes a new end-to-end deep learning model for fault diagnosis using three-axis vibration signals measured from facilities. The three-axis vibration signals measured in the time domain are used without domain transformations to train the end-to-end model. The proposed model is design...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2022-12, Vol.18 (12), p.8807-8817
Main Authors: Kim, Min Su, Yun, Jong Pil, Park, PooGyeon
Format: Article
Language:English
Subjects:
Convolution
Convolutional neural network (CNN)
Criteria
Data models
Data visualization
Deep learning
end-to-end model
Fault diagnosis
Feature extraction
Feature maps
Frequency domain analysis
Model accuracy
Rotary machines
Termites
Three axis
Vibration measurement
Vibrations
Visualization
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Summary:This article proposes a new end-to-end deep learning model for fault diagnosis using three-axis vibration signals measured from facilities. The three-axis vibration signals measured in the time domain are used without domain transformations to train the end-to-end model. The proposed model is designed to effectively extract feature maps of each X -, Y -, and Z -axis individually from the three-axis vibration signals using a grouped 1-D convolution. The feature maps extracted from each axis are composed of specific frequencies of each axis. Accordingly, the proposed model classifies faults based on the frequency characteristics of each axis. In addition, this article proposes a method to visualize the decision criteria of the proposed model in the frequency domain. Using the proposed model and the proposed visualization method, it is possible to grasp the degree to which specific frequencies of each X -, Y -, and Z -axis affect the decision criteria of the proposed model. The experiment is conducted by applying the proposed model to an open dataset with three-axis vibration signals measured from a rotary machine. Experimental results demonstrate that the proposed model achieves high accuracy and provides the explainability in each X -, Y -, and Z -axis using the visualized decision criteria in the frequency domain.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3147828