Intelligent fault diagnosis for diesel engine exhaust valve using support vector machine

Exhaust valve is one of the important parts of diesel engines, whose fault diagnosis attracts the researchers. However, the traditional intelligent methods lack better generation abilities, especially trained through a few samples. The infield-accuracy does not meet practical requirement. Support Vector Machine (SVM), which based on Statistic Learning Theory, has the adaptive generation ability. In this paper, the utilization of SVM's with good generation ability was adopted, and SVM was trained with the Wavelet Packets decomposed coefficients as the input index. Comparing with accuracy of different kernel's functions, the results showed that the SVM is superior to other intelligent fault diagnosis methods. The diagnosis accuracy is sensitive to the kernel function. The linear kernel's accuracy is 100%, which is the best kernel for exhaust valve fault diagnosis of diesel engine among commonly used kernel SVMs.