Gear fault diagnosis based on Gaussian correlation of vibrations signals and wavelet coefficients

Authors:
Amir Hosein Zamanian;Abdolreza Ohadi
Affiliations:
Acoustics Research Lab., Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave., 424 Tehran, Iran;Acoustics Research Lab., Mechanical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave., 424 Tehran, Iran
Venue:
Applied Soft Computing
Year:
2011

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Abstract

The features of non-stationary multi-component signals are often difficult to be extracted for expert systems. In this paper, a new method for feature extraction that is based on maximization of local Gaussian correlation function of wavelet coefficients and signal is presented. The effect of empirical mode decomposition (EMD) to decompose multi-component signals to intrinsic mode functions (IMFs), before using of local Gaussian correlation is discussed. The experimental vibration signals from two gearbox systems are used to show the efficiency of the presented method. Linear support vector machine (SVM) is utilized to classify feature sets extracted with the presented method. The obtained results show that the features extracted in this method have excellent ability to classify faults without any additional feature selection; it is also shown that EMD can improve or degrade features according to the utilized feature reduction method.