Recent developments of induction motor drives fault diagnosis using AI techniques

The paper presents a review, of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI). This review covers the application of expert systems, neural networks and fuzzy logic systems that can be integrated into each other and also with more traditional techniques to overcome specific problems. Usually a diagnostic procedure starts from a fault tree developed on the basis of the physical behaviour of the electrical system under consideration. In this phase the knowledge of well tested models able to simulate the electrical machine in different fault renditions is fundamental to obtain the patterns characterising the faults. Then 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 dataset for specific AI (neural networks, fuzzy logic or neuro-fuzzy) systems. The discussed methodologies, that play a general role in the diagnostic field, are applied to an induction machine, utilising as input signals the instantaneous voltages and currents. In addition, the supply converter is also considered to also incorporate in the diagnostic procedure the most typical failures of power electronic components. A brief description of the various techniques is provided, to highlight the advantages and the validity limits of using AI technologies. Some application examples are discussed and areas for future research are also indicated.