The Application of Support Vector Machines (SVM) to Fault Diagnosis of Marine Main Engine Cylinder Cover

Abstract

In this paper, a novel approach is proposed to diagnose faults of marine main engine cylinder cover. Considering vibration signal is highly related with various faults of cylinder cover, we propose to diagnose faults of marine main engine cylinder cover based on vibration signal from engine. First, a wavelet analysis method is used to characterize the power spectrum of the vibration signal. Next, principal component analysis (PCA) is used to extract the most distinctive feature for faults diagnosis. The extracted features are then fed into a set of pre-trained support vector machines (SVM) for fault diagnosis. Importantly, we use a cascade framework to organize a set of SVMs, for classifying different types of faults. The SVM is a new and effective method in fault diagnosis of marine main engine cylinder cover. Experimental results are presented to show that our proposed method is able to not only detect faults but also classify different types of faults accurately.