A combined image processing and Nearest Neighbor Algorithm tool for classification of incipient faults in induction motor drives. (August 2016)

• Record Type: Journal Article
• Authors: Bandyopadhyay, Indrayudh; Purkait, Prithwiraj; Koley, Chiranjib
• Is Part Of: Computers & electrical engineering. Volume 54(2016)
• Page Start: 296
• Page End: 312
• Publication Date: 2016-08
• Subjects: PWM induction motor drive -- IGBT -- Concordia pattern -- Shape context -- Incipient fault -- Nearest Neighbor Algorithm; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.01.014
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Incipient faults in Inverter of Induction motor drive are detected and classified. Concordia pattern is obtained from 3-phase output current of faulty inverter. Concordia pattern is converted to binary image. Shape context based Nearest Neighbor algorithm is used to classify the Concordia image. Abstract: Switching Devices such as IGBT used in Pulse Width Modulation (PWM) Inverter feeding an induction motor often suffer from different types of incipient faults like improper contact points, poor connections and problematic solder joints. These are due to ageing or prolonged operation in unfriendly environments. These faults need to be detected at their initial stages to prevent subsequent spreading of faults. In the present work, different variations of the above mentioned faulty cases in a PWM-Inverter have been studied by recording three phase inverter output current profiles and converting them to Concordia patterns. It has been observed that the Concordia patterns are quite different in shapes for different types of faults. A suitable image based shape descriptor has been applied to extract relevant information from these Concordia patterns. Finally, Nearest Neighbor Algorithm is employed on this information to identify the nature and location of faults. Performance of the algorithm is found to be quite satisfactory when its results are compared with two more related algorithms. Graphical abstract: