eprintid: 13700
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/37/00
datestamp: 2023-11-10 03:28:16
lastmod: 2023-11-10 03:28:16
status_changed: 2023-11-10 01:51:47
type: article
metadata_visibility: show
creators_name: Alshorman, O.
creators_name: Irfan, M.
creators_name: Saad, N.
creators_name: Zhen, D.
creators_name: Haider, N.
creators_name: Glowacz, A.
creators_name: Alshorman, A.
title: A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor
ispublished: pub
keywords: Accident prevention; Condition monitoring; Damage detection; Data fusion; Data handling; Data mining; Diagnosis; Expert systems; Fault detection; Induction motors; Roller bearings, Artificial intelligence methods; Artificial intelligent; Data processing techniques; Fault detection and diagnosis; Industrial environments; Intelligent diagnostics; Performance limitations; Rolling Element Bearing, Artificial intelligence
note: cited By 98
abstract: The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted. © 2020 Omar AlShorman et al.
date: 2020
publisher: Hindawi Limited
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096498124&doi=10.1155%2f2020%2f8843759&partnerID=40&md5=ddd35d96839a6f27fca0f14d634e6b4e
id_number: 10.1155/2020/8843759
full_text_status: none
publication: Shock and Vibration
volume: 2020
refereed: TRUE
issn: 10709622
citation:   Alshorman, O. and Irfan, M. and Saad, N. and Zhen, D. and Haider, N. and Glowacz, A. and Alshorman, A.  (2020) A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor.  Shock and Vibration, 2020.   ISSN 10709622