eprintid: 14045
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/40/45
datestamp: 2023-11-10 03:28:36
lastmod: 2023-11-10 03:28:36
status_changed: 2023-11-10 01:52:36
type: article
metadata_visibility: show
creators_name: Lemma, T.A.
creators_name: Omar, N.
creators_name: Gebremariam, M.A.
creators_name: Ahsan, S.
title: Anti-friction Bearing Malfunction Detection and Diagnostics Using Hybrid Approach
ispublished: pub
keywords: Antifriction bearings; Construction industry; Defects; Friction; Higher order statistics; Machine tools; Neural networks; Rotating machinery; Time domain analysis; Vibration analysis; Wavelet transforms, Abbott parameter; ANN; Bearing fault; Detection; Detection and diagnostics; Hybrid approach; Machine construction; Vibration; Vibration signal; Wavelet, Condition monitoring
note: cited By 1; Conference of 4th International Conference on Mechanical, Manufacturing and Plant Engineering, ICMMPE 2018 ; Conference Date: 14 November 2018 Through 15 November 2018; Conference Code:232589
abstract: Antifriction bearings are widely used in the industries especially in aircraft, machine tool, and construction industry. It holds and guides moving parts of the machine and reduces friction and wear. As they are one of the riskiest components in the rotating machinery, it puts challenges on the bearing health condition monitoring. The defects found in the bearings can lead to malfunctioning of the machinery and impact the level of production. This paper presents detection technique and diagnosis of bearing defects using a novel hybrid approach (continuous wavelet transform, Abbott�Firestone parameter, and artificial neural network). The vibration signals were obtained from Case Western Reserve University. MATLAB is used to analyse the vibration signals, test, and train the required models according to the chosen model structure. Various statistical features are extracted from the time domain namely kurtosis, skewness, root mean square, standard deviation, crest factor, and Abbott parameters to analyse and identify the bearing fault. The results demonstrate that the proposed method is effective in identifying the bearing faults. © 2020, Springer Nature Singapore Pte Ltd.
date: 2020
publisher: Springer Science and Business Media Deutschland GmbH
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075714423&doi=10.1007%2f978-981-13-8297-0_15&partnerID=40&md5=d23da30a438f8dd67ccf0cfa33cffd25
id_number: 10.1007/978-981-13-8297-0₁₅
full_text_status: none
publication: Lecture Notes in Mechanical Engineering
pagerange: 117-131
refereed: TRUE
isbn: 9789811382963
issn: 21954356
citation:   Lemma, T.A. and Omar, N. and Gebremariam, M.A. and Ahsan, S.  (2020) Anti-friction Bearing Malfunction Detection and Diagnostics Using Hybrid Approach.  Lecture Notes in Mechanical Engineering.  pp. 117-131.  ISSN 21954356