Gearbox Fault Identification and Classification with Convolutional Neural Networks
Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/390134 |
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| _version_ | 1832561142380101632 |
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| author | ZhiQiang Chen Chuan Li René-Vinicio Sanchez |
| author_facet | ZhiQiang Chen Chuan Li René-Vinicio Sanchez |
| author_sort | ZhiQiang Chen |
| collection | DOAJ |
| description | Vibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS) value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis. |
| format | Article |
| id | doaj-art-4c837893038845ae92c585072dfb3214 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-4c837893038845ae92c585072dfb32142025-02-03T01:25:51ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/390134390134Gearbox Fault Identification and Classification with Convolutional Neural NetworksZhiQiang Chen0Chuan Li1René-Vinicio Sanchez2Chongqing Engineering Laboratory for Detection, Control and Integrated System, School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing 400067, ChinaChongqing Engineering Laboratory for Detection, Control and Integrated System, School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing 400067, ChinaDepartment of Mechanical Engineering, Universidad Politécnica Salesiana, Cuenca, EcuadorVibration signals of gearbox are sensitive to the existence of the fault. Based on vibration signals, this paper presents an implementation of deep learning algorithm convolutional neural network (CNN) used for fault identification and classification in gearboxes. Different combinations of condition patterns based on some basic fault conditions are considered. 20 test cases with different combinations of condition patterns are used, where each test case includes 12 combinations of different basic condition patterns. Vibration signals are preprocessed using statistical measures from the time domain signal such as standard deviation, skewness, and kurtosis. In the frequency domain, the spectrum obtained with FFT is divided into multiple bands, and the root mean square (RMS) value is calculated for each one so the energy maintains its shape at the spectrum peaks. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Comparing with peer algorithms, the present method exhibits the best performance in the gearbox fault diagnosis.http://dx.doi.org/10.1155/2015/390134 |
| spellingShingle | ZhiQiang Chen Chuan Li René-Vinicio Sanchez Gearbox Fault Identification and Classification with Convolutional Neural Networks Shock and Vibration |
| title | Gearbox Fault Identification and Classification with Convolutional Neural Networks |
| title_full | Gearbox Fault Identification and Classification with Convolutional Neural Networks |
| title_fullStr | Gearbox Fault Identification and Classification with Convolutional Neural Networks |
| title_full_unstemmed | Gearbox Fault Identification and Classification with Convolutional Neural Networks |
| title_short | Gearbox Fault Identification and Classification with Convolutional Neural Networks |
| title_sort | gearbox fault identification and classification with convolutional neural networks |
| url | http://dx.doi.org/10.1155/2015/390134 |
| work_keys_str_mv | AT zhiqiangchen gearboxfaultidentificationandclassificationwithconvolutionalneuralnetworks AT chuanli gearboxfaultidentificationandclassificationwithconvolutionalneuralnetworks AT reneviniciosanchez gearboxfaultidentificationandclassificationwithconvolutionalneuralnetworks |