Vibration-based classification of centrifugal pumps using support vector machine and discrete wavelet transform

Due to the quick advancement of technology, application of different methods is highly required to maintain the high quality of production and health assessment of production lines. Hence, condition monitoring is widely used in the industry as an efficient approach. The purpose of the present study was to classify faults in centrifugal pumps using the vibration signal analysis and support vector machine (SVM) method. Vibration signals were decomposed in three levels by Daubechies wavelets, and a total of 44 descriptive statistical features were extracted from detail coefficients and approximation coefficients of the wavelets. In order to find the best model for fault classification of centrifugal pumps, parameters such as penalty, degree of polynomial, and width of the Gaussian radial basis function kernel (RBF kernel) were investigated. The classification results using the SVM method indicated that the maximum classification accuracy was 96.67 percent, which was obtained at an RBF kernel width of 0.1 and a penalty parameter value of 1.