relation: https://khub.utp.edu.my/scholars/10207/
title: Electrical Signature Analysis-Based Detection of External Bearing Faults in Electromechanical Drivetrains
creator: Shahriar, M.R.
creator: Borghesani, P.
creator: Tan, A.C.C.
description: An electrical signature analysis (ESA)-based method to detect bearing defects in electromechanical drivetrains (e.g., wind turbines) is proposed in this paper. The novelty of the proposed technique is justified by its capability to detect a defect that occurs in a drivetrain bearing, external to the machine. Unlike the traditional vibration-based method, the electrical measurements of the terminal machine are utilized in this case to perform the fault detection. The proposed ESA-based method is developed utilizing a new electrical signal model of bearing defect, proposed in this paper, based on the excitation of torsional resonances. The proposed ESA-based method and the signal model are validated through experimental tests. The proposed technique demonstrates excellent fault detection capability even at low operating speed, which is advantageous over the traditional vibration-based method in terms of sensor cost, installation complexity, and detection reliability. © 1982-2012 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2018
type: Article
type: PeerReviewed
identifier:   Shahriar, M.R. and Borghesani, P. and Tan, A.C.C.  (2018) Electrical Signature Analysis-Based Detection of External Bearing Faults in Electromechanical Drivetrains.  IEEE Transactions on Industrial Electronics, 65 (7).  pp. 5941-5950.  ISSN 02780046     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038853139&doi=10.1109%2fTIE.2017.2782240&partnerID=40&md5=3e4f9c48da3bc0b85eb19552ad65962f
relation: 10.1109/TIE.2017.2782240
identifier: 10.1109/TIE.2017.2782240