Real-Time Motor Fault Diagnosis Based on TCN and Attention
Motor failure can result in damage to resources and property. Real-time motor fault diagnosis technology can detect faults and diagnosis in time to prevent serious consequences caused by the continued operation of the machine. Neural network models can easily and accurately fault diagnose from vibra...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/4/249 |
| _version_ | 1827627949459767296 |
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| author | Hui Zhang Baojun Ge Bin Han |
| author_facet | Hui Zhang Baojun Ge Bin Han |
| author_sort | Hui Zhang |
| collection | DOAJ |
| description | Motor failure can result in damage to resources and property. Real-time motor fault diagnosis technology can detect faults and diagnosis in time to prevent serious consequences caused by the continued operation of the machine. Neural network models can easily and accurately fault diagnose from vibration signals. However, they cannot notice faults in time. In this study, a deep learning model based on a temporal convolutional network (TCN) and attention is proposed for real-time motor fault diagnosis. TCN can extract features from shorter vibration signal sequences to allow the system to detect and diagnose faults faster. In addition, attention allows the model to have higher diagnostic accuracy. The experiments demonstrate that the proposed model is able to detect faults in time when they occur and has an excellent diagnostic accuracy. |
| first_indexed | 2024-03-09T13:24:00Z |
| format | Article |
| id | doaj.art-12e8ab478bb5455a8f66831d0eb6e7dd |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T13:24:00Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-12e8ab478bb5455a8f66831d0eb6e7dd2023-11-30T21:26:03ZengMDPI AGMachines2075-17022022-03-0110424910.3390/machines10040249Real-Time Motor Fault Diagnosis Based on TCN and AttentionHui Zhang0Baojun Ge1Bin Han2National Engineering Research Center of Large Electric Machines and Heat Transfer Technology, Harbin University of Science and Technology, Harbin 150080, ChinaNational Engineering Research Center of Large Electric Machines and Heat Transfer Technology, Harbin University of Science and Technology, Harbin 150080, ChinaCollege of Computer and Control Engineering, Qiqihar University, Qiqihar 161000, ChinaMotor failure can result in damage to resources and property. Real-time motor fault diagnosis technology can detect faults and diagnosis in time to prevent serious consequences caused by the continued operation of the machine. Neural network models can easily and accurately fault diagnose from vibration signals. However, they cannot notice faults in time. In this study, a deep learning model based on a temporal convolutional network (TCN) and attention is proposed for real-time motor fault diagnosis. TCN can extract features from shorter vibration signal sequences to allow the system to detect and diagnose faults faster. In addition, attention allows the model to have higher diagnostic accuracy. The experiments demonstrate that the proposed model is able to detect faults in time when they occur and has an excellent diagnostic accuracy.https://www.mdpi.com/2075-1702/10/4/249TCNattentionfault diagnosismotor fault |
| spellingShingle | Hui Zhang Baojun Ge Bin Han Real-Time Motor Fault Diagnosis Based on TCN and Attention Machines TCN attention fault diagnosis motor fault |
| title | Real-Time Motor Fault Diagnosis Based on TCN and Attention |
| title_full | Real-Time Motor Fault Diagnosis Based on TCN and Attention |
| title_fullStr | Real-Time Motor Fault Diagnosis Based on TCN and Attention |
| title_full_unstemmed | Real-Time Motor Fault Diagnosis Based on TCN and Attention |
| title_short | Real-Time Motor Fault Diagnosis Based on TCN and Attention |
| title_sort | real time motor fault diagnosis based on tcn and attention |
| topic | TCN attention fault diagnosis motor fault |
| url | https://www.mdpi.com/2075-1702/10/4/249 |
| work_keys_str_mv | AT huizhang realtimemotorfaultdiagnosisbasedontcnandattention AT baojunge realtimemotorfaultdiagnosisbasedontcnandattention AT binhan realtimemotorfaultdiagnosisbasedontcnandattention |