Feature-based approach to monitor motor-operated valves used in nuclear power plants.
Degradation and failure of motor-operated valves (MOV) compromise operational readiness of the safety related systems of a nuclear power plant.
Motor current signature analysis has been found to be a selective and early indicator of a number of mechanical and electrical failures/abnormalities related to the MOV.
We present an unsupervised and fully automated method for the extraction of the motor current signature and an analysis to diagnose possible failures in MOV's. The reference line frequency is obtained by sampling the line voltage, and is used to demodulate the current waveform.
Having obtained the current signature, a set of features are extracted from the signature.
A discriminant analysis is performed on these primitives to detect and classify various types of failures.
The proposed method is nonintrusive, computationally efficient and yields good results.
It can be easily installed as a part of an expert system for preventive maintenance of MOV's in nuclear power plants.
Mots-clés Pascal : Application, Centrale nucléaire, Détection forme, Système expert, Prévention accident, Moteur électrique, Courant électrique, Analyse dommage, Démodulation, Forme onde grandeur électrique, Méthode calcul, Actionneur, Valve(mécanique), Théorie
Mots-clés Pascal anglais : Motor operated valves, Motor current signature analysis, Electrical failures, Electrical abnormalities, Reference line frequency, Current waveforms, Discriminant analysis, Application, Nuclear power plants, Feature extraction, Expert systems, Accident prevention, Electric motors, Electric currents, Failure analysis, Demodulation, Electric waveforms, Computational methods, Actuators, Valves (mechanical), Theory
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 96-0124691
Code Inist : 001D12A. Création : 199608.