Fault Detection and Diagnosis in a Set "Inverter-Induction Machine" Through Multidimensional Membership Function and Pattern Recognition

• Published in: IEEE transactions on energy conversion Vol. 24; no. 2; pp. 431 - 441
• Main Authors: Ondel, O., Clerc, G., Boutleux, E., Blanco, E.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Data standardization; diagnosis; Electric power; Electric variables measurement; Electrical fault detection; Engineering Sciences; Fault detection; Fault diagnosis; Feature extraction; induction machine; Induction machines; inverter; Inverters; membership function; Multidimensional systems; Nearest neighbor searches; nonexclusive decision rule; Pattern recognition; pattern recognition (PR); reliability index; induction machine; pattern recognition (PR); nonexclusive decision rule; reliability index; diagnosis; inverter; Data standardization; membership function
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2008.921559

Nowadays, electrical drives generally associate inverter and induction machine. Thus, these two elements must be taken into account in order to provide a relevant diagnosis of these electrical systems. In this context, the paper presents a diagnosis method based on a multidimensional function and pattern recognition (PR). Traditional formalism of the PR method has been extended with some improvements such as the automatic choice of the feature space dimension or a ldquononexclusiverdquo decision rule based on the k -nearest neighbors. Thus, we introduce a new membership function, which takes into account the number of nearest neighbors as well as the distance from these neighbors with the sample to be classified. This approach is illustrated on a 5.5 kW inverter-fed asynchronous motor, in order to detect supply and motor faults. In this application, diagnostic features are only extracted from electrical measurements. Experimental results prove the efficiency of our diagnosis method.