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Journal of Control Science and Engineering
Volume 2016, Article ID 1286318, 12 pages
Research Article

Neural Network Based Fault Detection and Diagnosis System for Three-Phase Inverter in Variable Speed Drive with Induction Motor

1Kunsan National University, Saemangeum Campus, Room No. 202/203, Osikdo-Dong, Gunsan-Si, Jeollabuk-Do 573-540, Republic of Korea
2School of Electronics and Information Engineering, Kunsan National University, Kunsan, Republic of Korea
3Department of Control and Robotics Engineering, Kunsan National University, Kunsan, Republic of Korea

Received 3 August 2016; Revised 5 October 2016; Accepted 12 October 2016

Academic Editor: Gang Li

Copyright © 2016 Furqan Asghar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, electrical drives generally associate inverter and induction machine. Therefore, inverter must be taken into consideration along with induction motor in order to provide a relevant and efficient diagnosis of these systems. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduces overall efficiency. In this paper, fault detection and diagnosis based on features extraction and neural network technique for three-phase inverter is presented. Basic purpose of this fault detection and diagnosis system is to detect single or multiple faults efficiently. Several features are extracted from the Clarke transformed output current and used in neural network as input for fault detection and diagnosis. Hence, some simulation study as well as hardware implementation and experimentation is carried out to verify the feasibility of the proposed scheme. Results show that the designed system not only detects faults easily, but also can effectively differentiate between multiple faults. These results prove the credibility and show the satisfactory performance of designed system. Results prove the supremacy of designed system over previous feature extraction fault systems as it can detect and diagnose faults in a single cycle as compared to previous multicycles detection with high accuracy.