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Fault diagnosis of antifriction bearing in internal combustion engine gearbox using data mining techniques

Author

Listed:
  • K. N. Ravikumar

    (National Institute of Technology Karnataka)

  • Suhas S. Aralikatti

    (National Institute of Technology Karnataka)

  • Hemantha Kumar

    (National Institute of Technology Karnataka)

  • G. N. Kumar

    (National Institute of Technology Karnataka)

  • K. V. Gangadharan

    (National Institute of Technology Karnataka)

Abstract

Ball bearing failure are most common failure in rotating machinery, which can be catastrophic. Hence obtaining early failure warning along with precise fault detection technique is at most important. Early detection and timely intervention are the key in condition monitoring for long term endurance of machine components. The early research has used signal processing and spectral analysis extensively for fault detection however data mining with machine learning is most effective in fault diagnosis, the same is presented in this paper. The vibration signals are acquired for an output shaft antifriction bearing in a two-wheeler gearbox operated at various loading conditions with healthy and fault conditions. Data mining is employed for these acquired signals. Statistical, discrete wavelet and empirical mode decomposition are employed for feature extraction process and J48 decision tree for feature selection. Classification is carried out using K*, Random forest and support vector machine algorithm. The classifiers are trained and tested using tenfold cross validation method to diagnose the bearing fault. A comparative study of feature extraction and classifiers are done to evaluate the classification accuracy. The results obtained from K* classifier with wavelet feature yielded better accuracy than rest other classifiers with classification accuracy 92.5% for bearing fault diagnosis.

Suggested Citation

  • K. N. Ravikumar & Suhas S. Aralikatti & Hemantha Kumar & G. N. Kumar & K. V. Gangadharan, 2022. "Fault diagnosis of antifriction bearing in internal combustion engine gearbox using data mining techniques," 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. 13(3), pages 1121-1134, June.
  • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01407-1
    DOI: 10.1007/s13198-021-01407-1
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    References listed on IDEAS

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    1. Miao He & David He & Jae Yoon & Thomas J Nostrand & Junda Zhu & Eric Bechhoefer, 2019. "Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach," Journal of Risk and Reliability, , vol. 233(3), pages 303-316, June.
    2. Huaqing Wang & Ruitong Li & Gang Tang & Hongfang Yuan & Qingliang Zhao & Xi Cao, 2014. "A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-13, October.
    3. C. K. Madhusudana & Hemantha Kumar & S. Narendranath, 2017. "Face milling tool condition monitoring using sound signal," 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. 8(2), pages 1643-1653, November.
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