Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods


1 Centre of Excellence in Complex and Nonlinear Dynamical Systems (CoE-CNDS), Veermata Jijabai Technological Institute, Mumbai, India

2 Mechanical Engineering Department, Veermata Jijabai Technological Institute, Mumbai, India


Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibration signals and are collected using experimental test rig for different input parameters like load, speed and bearing conditions. These features are ranked using two techniques, namely Decision Tree (DT) and Randomized Lasso (R Lasso), which are further used to form training and testing input feature sets to machine learning techniques.  It uses three ensemble machine learning techniques for AFB fault classification namely Random Forest (RF), Gradient Boosting Classifier (GBC) and Extra Tree Classifier (ETC). The impact of number of ranked features and estimators have been studied for ensemble techniques. The result showed that the classification efficiency is significantly influenced by the number of features but the effect of number of estimators is minor. The demonstrated ensemble techniques give more accuracy in classification as compared to tuned SVM with same experimental input data. The highest AFB fault classification accuracy 98% is obtained with ETC and DT feature ranking.


  1. Cerrada, M., Sánchez, R. V., Li, C., Pacheco, F., Cabrera, D., de Oliveira, J. V., and Vásquez, R. E., “A review on data-driven fault severity assessment in rolling bearings”, Mechanical Systems and Signal Processing, Vol. 99, (2018),169-196.
  2. Ming, A. B., Zhang, W., Qin, Z. Y., and Chu, F. L., “Fault feature extraction and enhancement of rolling element bearing in varying speed condition”, Mechanical Systems and Signal Processing, Vol. 76, (2016), 367-379.
  3. Chalouli, M., Berrached, N. E., and Denai, M., “Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction”, Journal of Failure Analysis and Prevention, Vol. 17(5),(2017), 1053-1066.
  4. Sugumaran, V., & Ramachandran, K. I., “Effect of number of features on classification of roller bearing faults using SVM and PSVM”, Expert Systems with Applications, Vol.  38(4), (2011), 4088-4096.
  5. Saimurugan, M., Ramachandran, K. I., Sugumaran, V., and Sakthivel, N. R., “Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine”, Expert Systems with Applications, Vol. 38 No. 4, (2011), 3819-3826.
  6. Sakthivel, N. R., Nair, B. B., and Sugumaran, V., “Soft computing approach to fault diagnosis of centrifugal pump”, Applied Soft Computing, Vol. 12 No.5, (2012), 1574-1581.
  7. Wang, H., Hong, R., Chen, J. and Tang, M., "Intelligent health evaluation method of slewing bearing adopting multiple types of signals from monitoring system", International Journal of Engineering, Transactions A: Basics, Vol. 28, No. 4, (2015), 573-581.
  8. Bansal, S., Sahoo, S., Tiwari, R., and Bordoloi, D. J., “Multiclass fault diagnosis in gears using support vector machine algorithms based on frequency domain data”, Measurement, Vol. 46(9), (2013), 3469-3481.
  9. Zhang, X., and Zhou, J. “Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines”, Mechanical Systems and Signal Processing, Vol. 41 No.1-2,(2013), 127-140.
  10. Yin, Z., and Hou, J. “Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes”, Neurocomputing, Vol. 174,(2016), 643-650.
  11. Sagi, O. and Rokach, L., Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 8 No. 4, (2018) p.e1249.
  12. Hu, J. and Min, J., “Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model”. Cognitive Neurodynamics, (2018), 1-10.
  13. Saleh, R. and Farsi, H., “Optimum Ensemble Classification for Fully Polarimetric Synthetic Aperture Radar Data Using Global-local Classification Approach”. International Journal of Engineering-Transactions B: Applications, Vol. 31 No. 2, (2018), 331-338.
  14. Murauer, B. and Specht, G., “Detecting Music Genre Using Extreme Gradient Boosting”. In Companion of the The Web Conference 2018. International World Wide Web Conferences Steering Committee., 1923-1927, (2018).
  15. Maier, O., Wilms, M., von der Gablentz, J., Krämer, U.M., Münte, T.F. and Handels, H., “Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences”. Journal of neuroscience methods, Vol. 240, (2015), 89-100.
  16. Kumar, S. and Sahoo, G., “A Random Forest Classifier based on Genetic Algorithm for Cardiovascular Diseases Diagnosis”. International Journal of Engineering-Transactions B: Applications, Vol. 30 No. 11, (2017), 1723-1729.
  17. Batista, L., Badri, B., Sabourin, R., and Thomas, M, “A classifier fusion system for bearing fault diagnosis”, Expert Systems with Applications, Vol. 40 No.17, (2013), 6788-6797
  18. Zhang, X., Wang, B., and Chen, X., “Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine”, Knowledge-Based Systems, Vol. 89, (2015), 56-85.
  19. Patel, R. K., and Giri, V. K., “Feature selection and classification of mechanical fault of an induction motor using random forest classifier”, Perspectives in Science, Vol. 8, (2016), 334-337.
  20. Heidari, M., “Fault Detection of Bearings Using a Rule-based Classifier Ensemble and Genetic Algorithm”, International Journal of Engineering Transactions A: Basics, Vol. 30 No. 4, (2017), 604-609.
  21. Friedman, J., Hastie, T., and Tibshirani, R., “The elements of statistical learning”, New York: Springer series in statistics, (2001).
  22. Geurts, P., Ernst, D., and Wehenkel, L. "Extremely randomized trees." Machine learning, Vol. 63, (2006), 3-42.
  23. Meinshausen, N., and Bühlmann, P., "Stability selection." Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 72 No. 4, (2010), 417-473.