An efficient diagnosis approach for bearing faults using sound quality metrics

Abstract

The perception of sound quality is an important source of information and can be used as a promising indicator to diagnose various faults in rotating machines. This work presented a methodology involving the detection of bearing faults using sound quality metrics. Head and Torso Simulator (HATS) was used to acquire the sound signals of different bearing fault conditions with a minimal background noise using a semi-anechoic chamber. After that, various sound quality features were extracted from the acquired signals, and descriptive analysis for five bearing conditions was presented based on these features. Moreover, these features were used to develop a self-adaptive fault diagnosis system using a Support Vector Machine (SVM). Experimental validation of the proposed method was also done with different background conditions and with different microphones. The results showed that the proposed methodology is reliable and effective for accurate fault diagnosis of bearings in a non-invasive manner.