Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults
Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/18/6065 |
| _version_ | 1827681335382114304 |
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| author | Shih-Lin Lin |
| author_facet | Shih-Lin Lin |
| author_sort | Shih-Lin Lin |
| collection | DOAJ |
| description | Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering. |
| first_indexed | 2024-03-10T07:14:39Z |
| format | Article |
| id | doaj.art-a179a28701924796a28fc0f92f288b34 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:14:39Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a179a28701924796a28fc0f92f288b342023-11-22T15:11:05ZengMDPI AGSensors1424-82202021-09-012118606510.3390/s21186065Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor FaultsShih-Lin Lin0Graduate Institute of Vehicle Engineering, National Changhua University of Education, No.1, Jin-De Road, Changhua City, Changhua County 50007, TaiwanMotor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.https://www.mdpi.com/1424-8220/21/18/6065VMD—ResNet101intelligent fault diagnosismotor fault |
| spellingShingle | Shih-Lin Lin Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults Sensors VMD—ResNet101 intelligent fault diagnosis motor fault |
| title | Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults |
| title_full | Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults |
| title_fullStr | Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults |
| title_full_unstemmed | Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults |
| title_short | Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults |
| title_sort | application combining vmd and resnet101 in intelligent diagnosis of motor faults |
| topic | VMD—ResNet101 intelligent fault diagnosis motor fault |
| url | https://www.mdpi.com/1424-8220/21/18/6065 |
| work_keys_str_mv | AT shihlinlin applicationcombiningvmdandresnet101inintelligentdiagnosisofmotorfaults |