  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.
