Induction motor bearing fault diagnostics using i-kaz™ and decision tree classification


  • M.S. Othman
  • M.Z. Nuawi
  • E. Mohamed



Condition monitoring, decision tree classification, bearing fault diagnosis, vibration signal, I-kaz™


In this paper, the I-kaz™ method is proposed for the detection of induction motor bearing faults using vibration signals, which has not been presented so far. The purpose of the study is to compare this new technique with the classical kurtosis method in the time domain and to validate the performance of the proposed I-kaz coefficient using a decision tree classification. Three bearing conditions are investigated; i.e. normal, ball fault and inner race fault; with a very small fault size (0.1778 mm). All faulty bearings are artificially damaged using electro-discharge machining and placed in the motor drive end side. The experimental test rig consists of a 2 HP induction motor, a torque transducer, a dynamometer and control electronics. Vibration data is obtained using an accelerometer and analyzed using MATLAB software for the time domain analysis which include the Ikaz graphical and coefficient comparison with time waveform and kurtosis values for all bearing conditions. Then, both features are used to train a conditional inference tree (CTree) fault classifier separately. The proposed I-kaz coefficient provides higher percentage differences between all faulty and normal bearings compared to kurtosis. However, the I-kaz graphic presents similar identification as the time waveform where only the inner race fault is distinguished from the normal bearing. The training classification results also revealed that the I-kaz coefficient is significantly better with an accuracy of 99.64% and a Kappa value of 0.9946 compared to kurtosis at only 63.57% and 0.4536, respectively. Furthermore, all test data were classified accordingly using the I-kaz coefficient whereas for kurtosis, only 65% are correctly classified with the 0.475 Kappa value. It is proved that the I-kaz™ method is suitable for induction bearing fault detection and is recommended as a classification feature, especially for the diagnostics of ball fault which is the most difficult to diagnose.




How to Cite

M. . Othman, M. Nuawi, and E. Mohamed, “ Induction motor bearing fault diagnostics using i-kaz™ and decision tree classification ”, Int. J. Automot. Mech. Eng., vol. 13, no. 2, pp. 3361–3372, Dec. 2022.