Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter. Issue 5 (1st May 2016)

• Record Type: Journal Article
• Authors: Godoy, Wagner Fontes; da Silva, Ivan Nunes; Goedtel, Alessandro; Palácios, Rodrigo Henrique Cunha; Lopes, Tiago Drummond
• Is Part Of: IET electric power applications. Volume 10:Issue 5(2016)
• Page Start: 430
• Page End: 439
• Publication Date: 2016-05-01
• Subjects: induction motors -- invertors -- rotors -- stators -- learning (artificial intelligence) -- multilayer perceptrons -- power engineering computing; intelligent tools -- rotor bars -- three‐phase induction motors fed -- inverter -- broken rotor bars -- frequency inverters -- predictive maintenance -- machine failures -- stator current signal -- dynamic acquisition rate -- learning machine techniques -- fuzzy ARTMAP network -- support vector machine -- k‐nearest neighbour -- multilayer perceptron network; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2015.0469
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : A comprehensive study of intelligent tools used to classify broken rotor bars in induction motors, which operate with three different types of frequency inverters, is presented. The diagnosis of defective rotor bars is a critical issue for the predictive maintenance of induction motors. A proper classification of these defects in their early stages of evolution is necessary for preventing major machine failures and production downtime. The proposed approach is performed by analysing the amplitude of the stator current signal in the time domain, using a dynamic acquisition rate based on machine frequency supply. To assess classification accuracy under the various severity levels of the faults, the performance of four different learning machine techniques is investigated: (i) fuzzy ARTMAP network; (ii) support vector machine (sequential minimal optimisation); (iii) k ‐nearest neighbour; and (iv) multilayer perceptron network. Results obtained from 1274 experimental tests are presented in order to validate the study, which considers a wide range of load conditions and operating frequencies. Experimental results presented in this study validate the robustness and efficacy of the proposed approach.