JOURNAL OF VIBROENGINEERING 1 Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review Adnan Althubaiti 1 , Faris Elasha 2 , Joao Amaral Teixeira 3 1, 2, 3 Centre for Propulsion Engineering, Cranfield University, Bedfordshire, United Kingdom 2 Faculty of Engineering, Environment and Computing, Coventry University, Coventry, United Kingdom 1 Corresponding author E-mail: 1 adnan.althubaiti@cranfield.ac.uk, 2 ac1027@coventry.ac.uk, 3 j.a.amaral.teixeira@cranfield.ac.uk Received 1 June 2021; received in revised form 22 September 2021; accepted 4 October 2021 DOI https://doi.org/10.21595/jve.2021.22100 Copyright © 2021 Adnan Althubiti, et al. 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. 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. Keywords: Bearing faults, time/frequency analysis, machine learning, diagnosis, prognosis, remaining useful life. 1. Introduction In recent years, condition monitoring, fault diagnosis and prognosis of equipment have become of increasing concern to industries using rotating machines. Early fault detection in rotating machines can avoid risks of damage and thus save expensive emergency repair costs. When operating as expected, all mechanical and electrical systems create a characteristic signal. If the operating conditions of a machine changes, this will lead to variance in that signal. In fact, differences in a normal signal can be considered an indication of an incipient fault. However, these changes may be so small that the signals are masked by the ambient noise produced by the system’s normal operation [1]. Machine Condition Monitoring (CM) is the procedure of monitoring several parameters being an indication of the mechanical condition of a rotating machine whilst it is in operation, such as vibration and temperature. Most new Condition Monitoring Systems are comprised of sensors and a system for acquiring data, integrated with software for signal analysis. A reliable online machinery CM system permits maintenance or corrective actions to be scheduled to prevent degradation of the machine’s performance, malfunctions, or even catastrophic failure [2]. The key purpose of a CM strategy is to enable immediate detection of any new damage in rotating machinery, such as bearings or gears. After the initial detection the CM should determine the location of the fault and its severity and predict the RUL of the component. CM offers the following benefits [3, 4]: 1) Avoid catastrophic failure, unscheduled maintenance and loss of production. 2) Reduce maintenance costs by minimising unnecessary interventions and overhauls. 3) Increase lifespan of components.