Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample
Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the effectiveness of DL models over shallow machine learning (SL) in terms of feature extraction, feature dimensional reduction and di...
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| Published in: | IEEE transactions on industrial informatics Vol. 16; no. 10; pp. 6263 - 6271 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1551-3203, 1941-0050 |
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| Abstract | Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the effectiveness of DL models over shallow machine learning (SL) in terms of feature extraction, feature dimensional reduction and diagnosis performance. Occasionally, during data acquisition, a problem with a sensor renders some of the data potentially unsuitable for further analysis, leaving only a small data sample. To compensate for this deficiency, a DL model based on a stacked sparse autoencoder (SSAE) model is designed to deal with limited sample data. In this article, the fault diagnosis system is developed based on time-frequency image pattern recognition. Therefore, two gearbox datasets are used to evaluate the proposed diagnosis system. The results from the experiments prove that the proposed system is capable of achieving high diagnostic accuracy even with limited sample data. The proposed fault diagnosis system achieved 100% and 99% diagnosis performance on experimental gearbox and wind turbine gearbox datasets, respectively. The proposed diagnosis system increased diagnosis performance between 10% and 20% over the standard SSAE model. In addition, the proposed model achieved higher diagnosis performance compared to deep neural network and convolutional neural networks models. |
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| AbstractList | Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the effectiveness of DL models over shallow machine learning (SL) in terms of feature extraction, feature dimensional reduction and diagnosis performance. Occasionally, during data acquisition, a problem with a sensor renders some of the data potentially unsuitable for further analysis, leaving only a small data sample. To compensate for this deficiency, a DL model based on a stacked sparse autoencoder (SSAE) model is designed to deal with limited sample data. In this article, the fault diagnosis system is developed based on time-frequency image pattern recognition. Therefore, two gearbox datasets are used to evaluate the proposed diagnosis system. The results from the experiments prove that the proposed system is capable of achieving high diagnostic accuracy even with limited sample data. The proposed fault diagnosis system achieved 100% and 99% diagnosis performance on experimental gearbox and wind turbine gearbox datasets, respectively. The proposed diagnosis system increased diagnosis performance between 10% and 20% over the standard SSAE model. In addition, the proposed model achieved higher diagnosis performance compared to deep neural network and convolutional neural networks models. |
| Author | Ahmad, Zair Asrar Bin Leong, Mohd Salman Saufi, Syahril Ramadhan Lim, Meng Hee |
| Author_xml | – sequence: 1 givenname: Syahril Ramadhan orcidid: 0000-0003-1317-7860 surname: Saufi fullname: Saufi, Syahril Ramadhan email: msramadhan93@gmail.com organization: School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia – sequence: 2 givenname: Zair Asrar Bin surname: Ahmad fullname: Ahmad, Zair Asrar Bin email: zair@mail.fkm.utm.my organization: School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia – sequence: 3 givenname: Mohd Salman surname: Leong fullname: Leong, Mohd Salman email: salman.leong@gmail.com organization: School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia – sequence: 4 givenname: Meng Hee surname: Lim fullname: Lim, Meng Hee email: limmenghee@gmail.com organization: Institute of Noise and Vibration, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia |
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| SubjectTerms | Analytical models Artificial neural networks Data models Datasets Deep learning Diagnostic systems Fault diagnosis Feature extraction gearbox Gearboxes image recognition limited data sample Machine learning Neural networks Object recognition Optimization Pattern recognition stacked sparse autoencoder (SSAE) System effectiveness Training Wind turbines |
| Title | Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample |
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