An efficient diagnosis approach for bearing faults using sound quality metrics

• Published in: Applied acoustics Vol. 195; p. 108839
• Main Authors: Mian, Tauheed, Choudhary, Anurag, Fatima, Shahab
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.06.2022
• Subjects: Ball bearing; Fault detection; HATS; Sound quality; SVM; Ball bearing; Sound quality; HATS; SVM; Fault detection
• ISSN: 0003-682X; 1872-910X
• DOI: 10.1016/j.apacoust.2022.108839

•A sound quality-based diagnosis method is proposed for bearing faults in a rotating machine.•Six sound quality features were used for the identification of faults in bearing.•Descriptive analysis of different bearing fault conditions was presented using sound quality parameters.•A quadratic SVM was used to develop a self-adaptive fault diagnosis system. 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.