Bibtex de la publication

@InProceedings{ BlReFaCh2006.1,
author = {Blodt, Martin and Regnier, Jeremi and Faucher, Jean and Chabert, Marie},
title = "{Maximum-Likelihood Parameter Estimation for Current-Based Mechanical Fault Detection in Induction Motors}",
booktitle = "{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, 14/05/2006-19/05/2006}",
year = {2006},
publisher = {IEEE},
address = {http://www.ieee.org/},
pages = {(electronic medium)},
language = {anglais},
note = { },
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 the estimation of the parameters of a sinusoidal phase mono-component signal. Second, the maximum likelihood 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.}
}