SVM Based Status Recognition of Electrical Parameters in Fault Diagnose for ESPCP

Various fault types and difficult diagnosis restricted the improvement of economic benefit and system efficiency of Electrical submersible progressing cavity pump(ESPCP) production system. A novel method for status recognition of electrical parameters in fault diagnosis of ESPCP by using support vector machine (SVM) based on small samples of statistical learning theory is presented. Application results show the proposed SVM classifier produces significant accuracy for classification of ESPCP electrical parameters.