Gear fault diagnosis based on Gaussian correlation of vibrations signals and wavelet coefficients
This work addresses gear fault diagnosis for mechanical systems, presenting an incremental improvement in feature extraction methods.
The paper tackled gear fault diagnosis by proposing a new feature extraction method based on maximizing local Gaussian correlation between wavelet coefficients and vibration signals, with results showing excellent classification ability using SVM without additional feature selection.
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.