Unstable engine vibration signal analysis using cyclostationarity and support vector machine theory

Abstract

According to the characteristics of unstable vibration signals, this paper proposes a combined approach to detect engine crank bearing mechanical faults by using cyclostationarity and support vector machine theory. The unstable vibration signals of engine accelerating process are analyzed by cyclostationarity theory. The fault diagnostic rules are generated by combining signal acquiring process and extracted fault features. And support vector machine is then trained. The result shows that the feature extraction is effectively realized by using cyclostationarity theory. Second order cyclical frequency bands of characteristic can be found corresponding to specific cyclical frequency. The support vector machine is superior to neural network because of the high classification precision and strong generalization ability for small samples. The diagnostic precision can be improved by means of optimizing parameters greatly.