| Paper: | SPTM-P2.3 | ||
| Session: | Detection | ||
| Time: | Tuesday, May 16, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, Classification Theory and Applications | ||
| Title: | Maximum-Likelihood Parameter Estimation for Current-Based Mechanical Fault Detection in Induction Motors | ||
| Authors: | Martin Blödt, LEEI - INP Toulouse, France; Marie Chabert, IRIT / ENSEEIHT / INP Toulouse, France; Jérémi Regnier, Jean Faucher, LEEI - INP Toulouse, France | ||
| Abstract: | 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. | ||
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