Rolling Element Bearing Fault Diagnosis using Empirical Mode Decomposition and Hjorth Parameters
Chhaya Grover, Neelam Turk
Abstract
The condition of rolling element bearing can be monitored by analyzing vibration signatures of machines at regular intervals. Many complex and computationally intensive bearing fault diagnosis schemes have been proposed in the past, extracting various features of vibration signals in time, frequency and time-frequency domains. This paper presents a time efficient and easy to implement scheme to diagnose bearing faults using Hjorth Parameters in time domain. The proposed scheme is experimentally validated on Case Western Reserve University Bearing Data Set, that comprises of normal faultless vibration signals and vibration signals with inner race, outer race and ball bearing faults. The feature vector is computed by calculating Hjorth parameters of representative Intrinsic Mode Functions obtained through Empirical mode decomposition of raw vibration signals. The data is then classified using four Rule-based classifiers. It is observed that PART classifier exhibits the best performance with an accuracy of 96.14% and 93.82 % for training and test datasets respectively. This paper proposes and validates that Hjorth Parameters, when used with Rule based machine learning algorithms, can be effectively used as fault sensitive features for rolling element bearing fault diagnosis.