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Controlling & diagnosis of asynchronous machines using
Kalman filter
The electrical machines use the
asynchronous motors more often because of their hardiness,
their
mass power and their low cost. Their maintenance and their diagnosis become
therefore
an
economic matter. It is important to develop methods of supervision to provide
the alarms and
information
on the detected defects, in order to lead corrective actions without
interruption of the
production
process. It is possible to lead this study thanks to the observation of some
parameters
of the machine and notably the magnetic fluxes and the electromagnetic
couple.
These
parameters are, however, difficult to measure and of this fact it is
preferable to estimate
them.
This estimation can be made by using the deterministic or stochastic
observers such as
the
extended Kalman filtering. Our
approach is based on the use of the methods of internal(Kalman filter)
and external
diagnosis(MCSA). The
methods of internal diagnosis permit the deletion of acoustics sensors,
vibrations sensors and
their replacement by software sensors allowing the reconstitution of the
measures using a
modeling
of the asynchronous motor. The Kalman filtering method of evaluation showed
an
important
development all along this project. It made the choice of the realization of
an electric
platform
and a dedicated pump of low cost. Our study has been achieved, using the
Labview
software,
including many interfaces of acquisitions, control and analysis
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