Intelligent Diagnosis Method for Centrifugal Pump System Using Vibration Signal and Support Vector Machine

Abstract

This paper proposed an intelligent diagnosis method for a centrifugal pump system using statistic filter, support vector machine (SVM), possibility theory, and Dempster-Shafer theory (DST) on the basis of the vibration signals, to diagnose frequent faults in the centrifugal pump at an early stage, such as cavitation, impeller unbalance, and shaft misalignment. Firstly, statistic filter is used to extract the feature signals of pump faults from the measured vibration signals across an optimum frequency region, and nondimensional symptom parameters (NSPs) are defined to represent the feature signals for distinguishing fault types. Secondly, the optimal classification hyperplane for distinguishing two states is obtained by SVM and NSPs, and its function is defined as synthetic symptom parameter (SSP) in order to increase the diagnosis’ sensitivity. Finally, the possibility functions of the SSP are used to construct a sequential fuzzy diagnosis for fault detection and fault-type identification by possibility theory and DST. The proposed method has been applied to detect the faults of the centrifugal pump, and the efficiency of the method has been verified using practical examples.