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

Author

Listed:
  • Zhongxin Chen

    (School of Mechanical Engineering, Shandong University, Jinan 250061, China
    Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan 250061, China)

  • Feng Zhao

    (School of Mechanical Engineering, Shandong University, Jinan 250061, China
    Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan 250061, China)

  • Jun Zhou

    (School of Mechanical Engineering, Shandong University, Jinan 250061, China
    Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan 250061, China)

  • Panling Huang

    (School of Mechanical Engineering, Shandong University, Jinan 250061, China
    Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan 250061, China)

  • Xutao Zhang

    (School of Mechanical Engineering, Shandong University, Jinan 250061, China
    Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Jinan 250061, China)

Abstract

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.

Suggested Citation

  • Zhongxin Chen & Feng Zhao & Jun Zhou & Panling Huang & Xutao Zhang, 2019. "Fault Diagnosis of Loader Gearbox Based on an ICA and SVM Algorithm," IJERPH, MDPI, vol. 16(23), pages 1-20, December.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4868-:d:293628
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    Citations

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    Cited by:

    1. Bahareh Tajiani & Jørn Vatn, 2023. "Adaptive remaining useful life prediction framework with stochastic failure threshold for experimental bearings with different lifetimes under contaminated condition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1756-1777, October.
    2. Ping Liu & Mengchu Xie & Jing Bian & Huishan Li & Liangliang Song, 2020. "A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction," IJERPH, MDPI, vol. 17(5), pages 1-24, March.

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