Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm
The use of the convolutional neural network for fault diagnosis has been a common method of research in recent years. Since this method can automatically extract fault features, it has played a good role in some research studies. However, this method has a clear drawback that the signals will be sig...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/9238908 |
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| _version_ | 1832566368461914112 |
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| author | Pengcheng Jiang Hua Cong Jing Wang Dongsheng Zhang |
| author_facet | Pengcheng Jiang Hua Cong Jing Wang Dongsheng Zhang |
| author_sort | Pengcheng Jiang |
| collection | DOAJ |
| description | The use of the convolutional neural network for fault diagnosis has been a common method of research in recent years. Since this method can automatically extract fault features, it has played a good role in some research studies. However, this method has a clear drawback that the signals will be significantly affected by working conditions and sample size, and it is difficult to improve diagnostic accuracy by directly learning faults, regardless of working conditions. It is therefore a research orientation worthy of a diagnosis of high precision defect in various working conditions. In this article, using a fine-grained classification algorithm, the operating conditions of the object system are considered an approximate classification. A specific failure in different working conditions is considered a beautiful classification. Samples of different faults in different working conditions are learned uniformly and the common characteristics are extracted from the convolutional network so that different faults of different working conditions can simultaneously be identified on the basis of the entire sample. Experimental results show that the method effectively uses the set of samples of the working conditions of the variables to obtain the dual recognition of defects and specific working conditions and the accuracy of the recognition is significantly higher than the method of learning regardless of working conditions. |
| format | Article |
| id | doaj-art-cdd6802bd01a448d94c827b234970751 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-cdd6802bd01a448d94c827b2349707512025-02-03T01:04:23ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/92389089238908Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN AlgorithmPengcheng Jiang0Hua Cong1Jing Wang2Dongsheng Zhang3Vehicle Engineering Department, Academy of Army Armored Force, Beijing, ChinaVehicle Engineering Department, Academy of Army Armored Force, Beijing, ChinaXi’an Jiaotong University, Xi’an, ChinaXi’an Jiaotong University, Xi’an, ChinaThe use of the convolutional neural network for fault diagnosis has been a common method of research in recent years. Since this method can automatically extract fault features, it has played a good role in some research studies. However, this method has a clear drawback that the signals will be significantly affected by working conditions and sample size, and it is difficult to improve diagnostic accuracy by directly learning faults, regardless of working conditions. It is therefore a research orientation worthy of a diagnosis of high precision defect in various working conditions. In this article, using a fine-grained classification algorithm, the operating conditions of the object system are considered an approximate classification. A specific failure in different working conditions is considered a beautiful classification. Samples of different faults in different working conditions are learned uniformly and the common characteristics are extracted from the convolutional network so that different faults of different working conditions can simultaneously be identified on the basis of the entire sample. Experimental results show that the method effectively uses the set of samples of the working conditions of the variables to obtain the dual recognition of defects and specific working conditions and the accuracy of the recognition is significantly higher than the method of learning regardless of working conditions.http://dx.doi.org/10.1155/2020/9238908 |
| spellingShingle | Pengcheng Jiang Hua Cong Jing Wang Dongsheng Zhang Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm Shock and Vibration |
| title | Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm |
| title_full | Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm |
| title_fullStr | Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm |
| title_full_unstemmed | Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm |
| title_short | Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm |
| title_sort | fault diagnosis of gearbox in multiple conditions based on fine grained classification cnn algorithm |
| url | http://dx.doi.org/10.1155/2020/9238908 |
| work_keys_str_mv | AT pengchengjiang faultdiagnosisofgearboxinmultipleconditionsbasedonfinegrainedclassificationcnnalgorithm AT huacong faultdiagnosisofgearboxinmultipleconditionsbasedonfinegrainedclassificationcnnalgorithm AT jingwang faultdiagnosisofgearboxinmultipleconditionsbasedonfinegrainedclassificationcnnalgorithm AT dongshengzhang faultdiagnosisofgearboxinmultipleconditionsbasedonfinegrainedclassificationcnnalgorithm |