Abstract
This paper presents a new model-based fault detection and diagnosis method for induction motors. The proposed strategy is based on generalized likelihood ratio (GLR), using statistical properties of residuals generated by extended Kalman filter. Under normal operation, the residuals follow Gaussian distribution, and they become non-Gaussian when a stator inter-turn short-circuit fault occurs in a motor. Unlike many existing methods that fail in realistic scenarios, the proposed method is robust to realistic conditions such as variable load, unbalanced input supply. To validate the proposed approach, computational programs are formulated and executed using MATLAB environment. Subsequently, experimental results are also included to validate the ability of the proposed strategy suitable for online fault detection and diagnosis.
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