Classification of Induction Machine Faults by Optimal Time-Frequency Representations

• Published in: IEEE transactions on industrial electronics (1982) Vol. 55; no. 12; pp. 4290 - 4298
• Main Authors: Lebaroud, A., Clerc, G.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Classification; Criteria; Diagnosis; Electric power; Engineering Sciences; Fault diagnosis; Faults; Flexibility; hidden Markov model (HMM); Hidden Markov models; Induction machines; induction motor; Induction motors; Machine windings; Machinery; Mathematical models; Representations; Rotors; Stator cores; Stator windings; Thermal stresses; time-frequency classification; Waveforms; Diagnosis; hidden Markov model (HMM); time–frequency classification; induction motor
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2008.2004666

This paper presents a new diagnosis method of induction motor faults based on time-frequency classification of the current waveforms. This method is based on a representation space, a selection criterion, and a decision criterion. In order to define the representation space, an optimized time-frequency representation (TFR) is designed from the time-frequency ambiguity plane. The selection criterion is based on Fisher's discriminant ratio, which allows one to maximize the separability between classes representing different faults. A distinct TFR is designed for each class. The following two classifiers were used for decision criteria: the Mahalanobis distance and the hidden Markov model. The flexibility of this method allows an accurate classification independent from the level of load. This method is validated on a 5.5-kW induction motor test bench.