Induction Machine Fault Detection and Classification Using Non-Parametric, Statistical-Frequency Features and Shallow Neural Networks

Kumar, Rahul R. and Cirrincione, G. and Cirrincione, Maurizio and Tortella, A. and Andriollo, M. (2020) Induction Machine Fault Detection and Classification Using Non-Parametric, Statistical-Frequency Features and Shallow Neural Networks. IEEE Transactions on Energy Conversion, TBC . TBC. ISSN 0885-8969

PDF - Accepted Version Restricted to Repository staff only Download (11MB)

Abstract

This paper presents a two-stage fault detection and classification scheme specifically designed for rotating electrical machines. The approach involves the use of new condition indicators that are specific to the frequency domain. The paper proposes two distinct features: one based on the extraction of peaks by using the prominence measure, a technique originating from the topology of mountains, and other based on the calculation of the occupied band power ratio for specific characteristic fault frequencies. A linear based feature reduction technique, the principal component analysis (PCA) has been employed to represent all the data. Afterwards, shallow neural networks have been used to detect and classify the three-phase current signals online. The effectiveness of the proposed scheme has been validated experimentally by using signals obtained with grid and inverter fed induction motors.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User:	Fulori Nainoca - Waqairagata
Date Deposited:	26 Jan 2021 23:49
Last Modified:	25 Nov 2022 05:20
URI:	http://repository.usp.ac.fj/id/eprint/12578

UNSPECIFIED

Actions (login required)

View Item