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Open AccessArticle

Fault Diagnosis of Loader Gearbox Based on an ICA and SVM Algorithm

by Zhongxin Chen 1,2, Feng Zhao 1,2, Jun Zhou 1,2,*, Panling Huang 1,2,* and Xutao Zhang 1,2
1
School of Mechanical Engineering, Shandong University, Jinan 250061, China
2
Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan 250061, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(23), 4868; https://doi.org/10.3390/ijerph16234868
Received: 5 November 2019 / Revised: 26 November 2019 / Accepted: 27 November 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Advances in the Field of Human Health and Environment)
When a part of the loader’s gearbox fails, this can lead to equipment failure due to the complex internal structure and the interrelationship between the parts. Therefore, it is imperative to research an efficient strategy for transmission fault diagnosis. In this study, the non-contact characteristics of noise diagnosis using sound intensity probes were used to collect noise signals generated under gear breaking conditions. The independent component analysis (ICA) technique was applied for feature extraction from the original data and to reduce the correlation between the signals. The correlation coefficient between the independent components and the source data was used as the input parameters of the support vector machine (SVM) classifier. The separation of the independent components was achieved by MATLAB simulation. The misdiagnosis rate was 5% for 40 sets of test data. A 13-point test platform for noise testing of the loader gearbox was built according to Chinese national standards. Source signals under the normal and fault conditions were analyzed separately by ICA and SVM algorithms. In this case, the misdiagnosis rate was 7.5% for the 40 sets of experimental test data. This proved that the proposed method could effectively realize fault classification and recognition. View Full-Text
Keywords: independent component analysis; support vector machines; feature extraction; fault diagnosis; gearbox independent component analysis; support vector machines; feature extraction; fault diagnosis; gearbox
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Chen, Z.; Zhao, F.; Zhou, J.; Huang, P.; Zhang, X. Fault Diagnosis of Loader Gearbox Based on an ICA and SVM Algorithm. Int. J. Environ. Res. Public Health 2019, 16, 4868.

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