Maximum-Likelihood Parameter Estimation for Current-Based Mechanical Fault Detection in Induction Motors

This paper proposes a new method for mechanical fault detection in induction motors. The detection strategy is based on the estimation of a particular stator current parameter. The considered mechanical faults cause periodic load torque oscillations leading to a sinusoidal phase modulation of the stator current. The modulation index is related to the fault severity and can be used as a fault indicator. First, a simplified stator current model is proposed. The problem is then equivalent to the parameter estimation of a sinusoidal phase mono-component signal. Second, the maximum likelihood estimator is implemented using evolution strategies for optimization. The Cramer-Rao lower bounds are calculated and compared to the estimator performance through simulations. The estimation procedure is studied on experimental stator current signals from faulty and healthy motors