Intelligent Fault Diagnosis of Nuclear grade Electric Equipment Based on Quantum Genetic Support Vector Machine

Abstract

Nuclear grade electric equipment is the key operating equipment of reactors in nuclear islands, and its reliable operation is a prerequisite to guarantee the operation of nuclear reactors. In order to effectively diagnose the faults of nuclear grade electric equipment, an intelligent fault diagnosis method based on QGA-SVM (Quantum Genetic Support Vector Machine) for nuclear grade electric equipment is proposed. Firstly, EEMD (Ensemble Empirical Mode Decomposition) and vibration eigenvalues calculation are carried out for the vibration signals collected under normal operation state and different fault degrees of nuclear grade equipment. Secondly, power eigenvalues calculation is carried out for the power signals collected under normal operation state and different fault degrees of nuclear grade equipment. Then, QGA((Quantum Genetic algorithm) and SVM (Support Vector Machine) are established to build an intelligent fault diagnosis model for nuclear grade electric equipment, and the operation eigenvalues is used as model input parameters. The results show that the proposed algorithm can efficiently and intelligently diagnose the faults of nuclear grade electric equipment, and the proposed method has certain significance for the fault diagnosis of electric equipment in other fields.