Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review

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dc.contributor.authorAlthubaiti, Adnan
dc.contributor.authorElasha, Faris
dc.contributor.authorAmaral Teixeira, Joao
dc.date.accessioned2022-02-16T09:53:09Z
dc.date.available2022-02-16T09:53:09Z
dc.date.issued2021-11-26
dc.description.abstractThere is an ever-increasing need to optimise bearing lifetime and maintenance cost through detecting faults at earlier stages. This can be achieved through improving diagnosis and prognosis of bearing faults to better determine bearing remaining useful life (RUL). Until now there has been limited research into the prognosis of bearing life in rotating machines. Towards the development of improved approaches to prognosis of bearing faults a review of fault diagnosis and health management systems research is presented. Traditional time and frequency domain extraction techniques together with machine learning algorithms, both traditional and deep learning, are considered as novel approaches for the development of new prognosis techniques. Different approaches make use of the advantages of each technique while overcoming the disadvantages towards the development of intelligent systems to determine the RUL of bearings. The review shows that while there are numerous approaches to diagnosis and prognosis, they are suitable for certain cases or are domain specific and cannot be generalised.en_UK
dc.identifier.citationAlthubaiti A, Elasha F, Amaral Teixeira J. (2022) Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review, Journal of Vibroengineering, Volume 24, Issue 1, February 2022, pp. 46-74en_UK
dc.identifier.eissn2538-8460
dc.identifier.issn1392-8716
dc.identifier.urihttps://doi.org/10.21595/jve.2021.22100
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17571
dc.language.isoenen_UK
dc.publisherJVE Internationalen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBearing faultsen_UK
dc.subjecttime/frequency analysisen_UK
dc.subjectmachine learningen_UK
dc.subjectdiagnosisen_UK
dc.subjectprognosisen_UK
dc.subjectremaining useful lifeen_UK
dc.titleFault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a reviewen_UK
dc.typeArticleen_UK

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