Compound gear-bearing fault feature extraction using statistical features based on time-frequency method

A small incipient fault in gear and bearing causes multiple faults in gear-bearing system leading to catastrophic failure. The purpose of present study is to explore more complex situation of compound gear-bearing fault. The compound faults such as a fault in the inner and outer race of bearing along with two teeth of gear having corner damage or three teeth of gear having corner damage, etc. are investigated using experimentation. To improve the effectiveness of diagnosis, vibration measurement is done at different speed and load condition. This paper proposes new compound fault features, extracted from continuous and discrete wavelet transform of vibration signal. The methodology consist of proposing the features in time-frequency domain and comparison of its diagnostic potential with respect to the features extracted from time and frequency domain for compound fault identification using three different classifiers. The fault classification accuracy of these features is found to be better than the conventional time and frequency domain parameters.