Recent developments of induction motor drives fault diagnosis usingAI techniques
Filippetti, F.; Franceschini, G.; Tassoni, C.; Vas, P.
Industrial Electronics, IEEE Transactions on
Volume 47, Issue 5, Oct 2000 Page(s):994 - 1004
Digital Object Identifier 10.1109/41.873207
Summary:This paper presents a review of the developments in the field of
diagnosis of electrical machines and drives based on artificial
intelligence (AI). It covers the application of expert systems,
artificial neural networks (ANNs), and fuzzy logic systems that can be
integrated into each other and also with more traditional techniques.
The application of genetic algorithms is considered as well. In general,
a diagnostic procedure starts from a fault tree developed on the basis
of the physical behavior of the electrical system under consideration.
In this phase, the knowledge of well-tested models able to simulate the
electrical machine in different fault conditions is fundamental to
obtain the patterns characterizing the faults. The fault tree navigation
performed by an expert system inference engine leads to the choice of
suitable diagnostic indexes, referred to a particular fault, and
relevant to build an input data set for specific AI (NNs, fuzzy logic,
or neuro-fuzzy) systems. The discussed methodologies, that play a
general role in the diagnostic field, are applied to an induction
machine, utilizing as input signals the instantaneous voltages and
currents. In addition, the supply converter is also considered to
incorporate in the diagnostic procedure the most typical failures of
power electronic components. A brief description of the various AI
techniques is also given; this highlights the advantages and the
limitations of using AI techniques. Some applications examples are also
discussed and areas for future research are also indicated
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