Fault Diagnosis of Heavy-Loaded AGV Based on Digital Mirror
Automated guided vehicles (AGVs) have emerged as crucial machinery in enterprise production and transportation as intelligent factories have gained traction. This study proposes a multi-source data enhancement fusion convolutional neural network (ME-CNN) approach for the mechanical fault diagnosis o...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10741516/ |
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| author | Yiwen Zhang Yan Gao Xinming Zhang Linsen Song Baoyan Zhao Jingru Liu Longkai Liang Jing Jiao |
| author_facet | Yiwen Zhang Yan Gao Xinming Zhang Linsen Song Baoyan Zhao Jingru Liu Longkai Liang Jing Jiao |
| author_sort | Yiwen Zhang |
| collection | DOAJ |
| description | Automated guided vehicles (AGVs) have emerged as crucial machinery in enterprise production and transportation as intelligent factories have gained traction. This study proposes a multi-source data enhancement fusion convolutional neural network (ME-CNN) approach for the mechanical fault diagnosis of heavy-duty AGVs. Specifically, an improved stacked autoencoder (SAE) was employed to augment the available data and address the issue of insufficient data samples during fault diagnosis. Subsequently, the augmented data were processed using a continuous wavelet transform (CWT). This method employs a combination of diverse measurement signals and data sources to facilitate a more precise and resilient diagnosis of heavy-duty AGVs. In addition, to further promote the coverage of smart factories, this method is combined with digital mirroring, and the virtual platform receives data to monitor the status of heavy-loaded AGVs in real-time and determine the type of fault. Following testing, the system response time was less than 500 ms, allowing for the timely diagnosis of faults. Finally, the validity of the method was verified by comparing a factory-heavy load AGV with various other algorithms, resulting in an accuracy rate of 97.0% for fault diagnosis. |
| format | Article |
| id | doaj-art-628f9b0b686c4bf5b87c76f3be2409af |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-628f9b0b686c4bf5b87c76f3be2409af2025-08-20T02:13:52ZengIEEEIEEE Access2169-35362024-01-011216289416290710.1109/ACCESS.2024.349067010741516Fault Diagnosis of Heavy-Loaded AGV Based on Digital MirrorYiwen Zhang0https://orcid.org/0009-0005-7387-917XYan Gao1https://orcid.org/0009-0006-0926-4485Xinming Zhang2https://orcid.org/0000-0002-6713-1430Linsen Song3https://orcid.org/0000-0002-2933-2549Baoyan Zhao4Jingru Liu5Longkai Liang6Jing Jiao7School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, ChinaSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, ChinaFAW Tooling Die Manufacturing Corporation, Changchun, ChinaFAW Tooling Die Manufacturing Corporation, Changchun, ChinaSchool of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, ChinaChangchun Dazheng Bocae Automotive Equipment Company Ltd., Changchun, Jilin, ChinaAutomated guided vehicles (AGVs) have emerged as crucial machinery in enterprise production and transportation as intelligent factories have gained traction. This study proposes a multi-source data enhancement fusion convolutional neural network (ME-CNN) approach for the mechanical fault diagnosis of heavy-duty AGVs. Specifically, an improved stacked autoencoder (SAE) was employed to augment the available data and address the issue of insufficient data samples during fault diagnosis. Subsequently, the augmented data were processed using a continuous wavelet transform (CWT). This method employs a combination of diverse measurement signals and data sources to facilitate a more precise and resilient diagnosis of heavy-duty AGVs. In addition, to further promote the coverage of smart factories, this method is combined with digital mirroring, and the virtual platform receives data to monitor the status of heavy-loaded AGVs in real-time and determine the type of fault. Following testing, the system response time was less than 500 ms, allowing for the timely diagnosis of faults. Finally, the validity of the method was verified by comparing a factory-heavy load AGV with various other algorithms, resulting in an accuracy rate of 97.0% for fault diagnosis.https://ieeexplore.ieee.org/document/10741516/Intelligent manufacturingheavy-loaded AGVmachine learningdigital mirroring |
| spellingShingle | Yiwen Zhang Yan Gao Xinming Zhang Linsen Song Baoyan Zhao Jingru Liu Longkai Liang Jing Jiao Fault Diagnosis of Heavy-Loaded AGV Based on Digital Mirror IEEE Access Intelligent manufacturing heavy-loaded AGV machine learning digital mirroring |
| title | Fault Diagnosis of Heavy-Loaded AGV Based on Digital Mirror |
| title_full | Fault Diagnosis of Heavy-Loaded AGV Based on Digital Mirror |
| title_fullStr | Fault Diagnosis of Heavy-Loaded AGV Based on Digital Mirror |
| title_full_unstemmed | Fault Diagnosis of Heavy-Loaded AGV Based on Digital Mirror |
| title_short | Fault Diagnosis of Heavy-Loaded AGV Based on Digital Mirror |
| title_sort | fault diagnosis of heavy loaded agv based on digital mirror |
| topic | Intelligent manufacturing heavy-loaded AGV machine learning digital mirroring |
| url | https://ieeexplore.ieee.org/document/10741516/ |
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