Bearing Fault Diagnosis Using Feature Ranking Methods and Fault Identification Algorithms

Diagnoses of bearing faults are important to avoid catastrophic failures in rotating machines. This paper presents a methodology to detect various bearing faults from the measured vibration signal. Features such as kurtosis, skewness, mean, root mean square and complexity measure such as Shannon entropy are calculated from time domain,frequency domain and discrete wavelet transform. In total 40 features are calculated from bearing conditions such as Healthy bearing, Inner race fault, Outer race fault and Ball fault. Feature ranking methods such as Chisquare, ReliefF method are used to select most informative feature and subsequently to reduce size of feature vector. Comparison has been made between feature ranking methods and classifiers to obtain best diagnosis result with reduce feature set. Our results shows good fault identification accuracy with minimum number of features.