Sensitivity Analysis of SVM Kernel Functions in Machinery Condition Classification

Abstract

The excellent generalisation ability of the Support Vector Machine (SVM) algorithm has made it one of the most popular statistical learning theories in supervised machine learning. The classification accuracy and effectiveness of SVM is highly sensitive to the kernel function used during the training process. This paper compares linear, polynomial, and Gaussian kernel functions for evaluating their contribution to SVM for accurately and effectively classifying healthy and faulty status of rotating machinery. A three-phase induction motor and a four-stroke diesel engine were considered as the machinery for this study. Acoustic signals coming from these machines were collected using microphones and Fast Fourier Transform (FFT) was used to extract the magnitudes of the dominant frequency components of the signals. The extracted ominant frequency components are considered as acoustic signatures and their variations are taken as condition monitoring parameters. The results show that with the second-order polynomial kernel function, SVM achieved an accuracy of at least 2.4% greater than the other kernel functions with 1.2% less training time. Furthermore, the third-order polynomial kernel function found to be the second best choice.