A neural network-based model for MCSA of inter-turn short-circuit faults in induction motors and its power hardware in the loop simulation. (July 2021)

• Record Type: Journal Article
• Authors: Mejia-Barron, Arturo; Tapia-Tinoco, Guillermo; Razo-Hernandez, Jose R.; Valtierra-Rodriguez, Martin; Granados-Lieberman, David
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: Induction motor -- Interturn short-circuit fault -- Motor current signal analysis -- Neural network -- Power hardware in the loop; 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.2021.107234
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Induction motors (IMs) are one of the most commonly used rotating machines in industry. In order to avoid downtimes and economical losses, development of condition monitoring systems is of paramount importance. Inter-turn short circuit (ITSC) faults are one of the most commonly occurring faults in IMs. In this regard, motor current signature analysis (MCSA) is a low cost and noninvasive technique, requiring only the current signal to perform the condition monitoring. Also, the development of tools that emulate IM faults, allow the design, calibration and validation of MCSA based techniques. In this work, a multilayer neural network-based model to reproduce the current signatures associated with ITSC fault conditions is presented. The model considers both five severities of ITSC faults and four torque levels. This model is also implemented on a power hardware in the loop scheme to provide a novel tool to test condition monitoring systems of IMs. Graphical abstract: Image, graphical abstract