Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example
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Published:2023-06-30
Issue:13
Volume:13
Page:7763
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Chen Ming-Huang1, Chen Shang-Liang1, Lin Yu-Sheng2ORCID, Chen Yu-Jen2ORCID
Affiliation:
1. Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan 70101, Taiwan 2. Department of Mechanical Engineering, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
Abstract
Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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