Abstract
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.
Highlights
Due to the advancement of technology, industrial equipment is increasingly made more complex, requiring more careful attention to be paid to them as their failure and breakdown can cause significant costs
Various methods have been introduced and implemented for single-sensor condition monitoring based on a single characteristic such as vibration [3,4,5] and acoustic [6,7,8] using classifiers such as support vector machines (SVMs) [5, 9, 10] and artificial neural networks (ANNs) [11]
The results indicate that by decreasing the kernel width from 1 to 0.1, the accuracy of the SVM in detecting faults significantly increases, such that the best accuracy was obtained for a kernel width of 0.1
Summary
Due to the advancement of technology, industrial equipment is increasingly made more complex, requiring more careful attention to be paid to them as their failure and breakdown can cause significant costs Factors such as reliability, accessibility, reduction of failure duration, and reparability of equipment are of great importance. Fault detection was usually based on analysis of either vibration or acoustic data [2]. Traditional spectral analysis techniques based on the Fourier transform provide a good description of stationary and pseudo stationary signals. The best approach for analysis of a non stationary vibration signal in a time-frequency domain is a wavelet transform
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