Airgap Flux Based Detection and Classification of Induction Motor Rotor and Load Defects During the Starting Transient

Abstract

There has been active research on motor current signature analysis (MCSA) for over the last 30+ years, as it can provide remote monitoring of rotor and load defects using the current sensor available in the motor control center. However, many years of experience has shown that 1) rotor eccentricity and load defects cannot be distinguished and 2) false rotor cage fault indications are common in the field. There is a recent trend toward integrating "smart" self-diagnostics in electric machines through embedded sensors, especially in applications such as submersible pumps or nuclear plants where motor inspection is difficult. The objective of this article is to evaluate airgap flux measurement as an option for providing sensitive and reliable monitoring of motor and load defects. A new method based on the analysis of the airgap flux search coil voltage during motor starting is proposed for reliable detection and classification of rotor cage, eccentricity, and mechanical defects in the load. Experimental testing is provided to verify the claims made on the proposed method, for cases where MCSA fails.