Application of an integrated power line signal analysis methodology to the detection of major rotating machine abnormalities in nuclear power plants

Abstract

An integrated and improved method to detect and identify the abnormality of motor driven rotating machinery in nuclear power plants (NPPs) using power line signal analysis is suggested in this work. The primary goal of this work is to improve the motor current signature analysis (MCSA) method that has been used as an alternative or supplement of the conventional vibration monitoring system (VMS). Through this work, the integrated system using both modulated flux density model (MFDM) and rotating flux model (RFM) is proposed. The MFDM is based on the fact that the major mechanical vibration of rotating machines can be normalized to the motor air-gap eccentricity and the modulation of air-gap flux density. Therefore, if the major defect such as bearing defect or the shaft deformation is present, it is identifiable through the power line signal resulting from the modulated magnetic density. Moreover, the broken rotor bar state or rotor eccentricity due to electrical imbalance can be analyzed using the RFM. The other important feature of this system is an automated abnormality detection and diagnosis algorithm. It is possible to diagnose the abnormality without relying on experts in NPPs. The verification is done through varying load/torque test experiment as well as via computer simulation in this work. The experimental results show that they are in good agreement with the simulated results. (C) 2004 Elsevier B.V. All rights reserved.