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

Adnan Althubaiti, Faris Elasha, Joao Amaral Teixeira

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
331 Downloads (Pure)

Abstract

There 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.

Original languageEnglish
Pages (from-to)46-74
Number of pages29
JournalJournal of Vibroengineering
Volume24
Issue number1
Early online date26 Nov 2021
DOIs
Publication statusPublished - Feb 2022

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords

  • Bearing faults
  • Diagnosis
  • Machine learning
  • Prognosis
  • Remaining useful life
  • Time/frequency analysis

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanical Engineering

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