relation: https://khub.utp.edu.my/scholars/8512/
title: An Online Fault Diagnosis System for Induction Motors via Instantaneous Power Analysis
creator: Irfan, M.
creator: Saad, N.
creator: Ibrahim, R.
creator: Asirvadam, V.S.
creator: Magzoub, M.
description: This article aims to provide a new noninvasive method for the online diagnosis of bearing-localized faults under various loading conditions of the induction motors via instantaneous power analysis. The instantaneous noise variations and sensor offsets are considered to be one of the common factors that yield erroneous fault tracking in an online condition monitoring and fault diagnosis system. An adaptive threshold scheme has been designed to tackle the sensor offsets and instantaneous noise variations for reliable decision making on the existence of fault signatures in an arbitrary environment conditions. The performance of the designed threshold scheme has been evaluated on a motor with various bearing defects operating under various loading conditions. Detailed theoretical and experimental evaluations of several bearing-localized faults are presented. The results indicate the viability and effectiveness of the proposed method. © 2017 Society of Tribologists and Lubrication Engineers.
publisher: Taylor and Francis Inc.
date: 2017
type: Article
type: PeerReviewed
identifier:   Irfan, M. and Saad, N. and Ibrahim, R. and Asirvadam, V.S. and Magzoub, M.  (2017) An Online Fault Diagnosis System for Induction Motors via Instantaneous Power Analysis.  Tribology Transactions, 60 (4).  pp. 592-604.  ISSN 10402004     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988601089&doi=10.1080%2f10402004.2016.1190043&partnerID=40&md5=1f460057adb11088d952924267c9a109
relation: 10.1080/10402004.2016.1190043
identifier: 10.1080/10402004.2016.1190043