Bearing failure prediction using audio signal analysis based on SVM algorithms

8th Conference on Material Science and Engineering

工业技术（总论）;材料科学

Marin, F.B.^1 ; Solomon, C.^1 ; Marin, M.^1

Faculty of Engineering, Dunrea de Jos University of Galati, Domneasc Street, 47, Galati

RO-800008, Romania^1

关键词: Accurate estimation;    Accurate prediction;    Acoustic emission signal;    Audio signal analysis;    Bearing failures;    Failure of bearings;    Prediction accuracy;    Sensitive application;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/485/1/012012/pdf
DOI  :  10.1088/1757-899X/485/1/012012

来源: IOP

【 摘 要 】

Bearings are machine elements used in a wide variety of applications including transportation. The accurate prediction of a bearing failure is important to sensitive applications to secure its safety during the service life. Bearing failure prediction is useful both in bearing testing phase as well as in case of lifetime use. Real-time Audio signal analysis and advanced algorithms are able to identify the incipient failure, caused by defects, fatigue, overload or poor maintenance. Audio signal analysis and processing remains a domain where technique and algorithm needs to be developed. In this paper is presented a proof-of-concept technique and equipment developed to predict failure of bearings in case of testing phase. For this study, acoustic emission signals were measured and analyzed during life testing of bearing while other sound source are also recorded. Correlation between the acoustic emission patterns were identified in order to identify noise signal and identify the signal associated with bearing degradation. The developed solution to isolate other sounds signals means that the technique could be used while lifetime of the bearings. The results of this study provide evidence that accurate estimation of the failure of various bearings is possible by processing the vibration signal acquired from a single point, even in case of multiple sound sources are present and introduce noise in signal processing. The SVM classifier provides at least 92% mean accuracy. The influence of model on prediction accuracy has also been discussed in the work.