Local Fault Assessment in a Helical Geared System via Sound and Vibration Parameters Using Multiclass SVM Classifiers

A gear system transmits power by means of meshing gear teeth and is conceptually simple and effective in power transmission. Thus typical applications include electric utilities, ships, helicopters, and many other industrial applications. Monitoring the condition of large gearboxes in industries has attracted increasing interest in the recent years owing to the need for decreasing the downtime on production machinery and for reducing the extent of secondary damage caused by failures. This paper addresses the development of a condition monitoring procedure for a gear transmission system using artificial neural networks (ANNs) and support vector machines (SVMs). Seven conditions of the gear were investigated: healthy gear and gear with six stages of depthwise wear simulated on the gear tooth. The features extracted from the measured vibration and sound signals were mean, root mean square (rms), variance, skewness, and kurtosis, which are known to be sensitive to different degrees of faults in rotating machine elements. These characteristics were used as an input features to ANN and SVM. The results show that the multilayer feed forward neural network and multiclass support vector machines can be effectively used in the diagnosis of various gear faults.