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Personal profile

Biography

I Joined Coventry University on September 2015 as Lecturer in Dynamics. Previously I was Research Fellow at Cranfield University. I have over 10 years experince as a Mechanical Engineer in Power Generation Industry, through my previous career, I participated in, and headed, many committees for Power stations maintenance and overhauls management. I have a degree in Mechanical Engineering from Sudan University of Scince and Technology (BSc), MSc in Mechanical Engineering Design from The University of Manchester and Ph.D. in Mechanical Engineering from Cranfield University. My research area is rotating machines diagnosis and prognosis. Recent involvement in the renewables area includes contributions towards the design of condition monitoring system for tidal turbines.

Research:

Condition monitoring, diagnosis, prognosis, and intelligent health management are effective means to reducing the downtime and maintenance cost and improving the reliability of machines. These important issues have drawn more and more attention during the last decade and significant research effort is being taken by both academia and industry to advance the technologies for condition monitoring, diagnosis, prognosis, and health management of rotating machines.

My research Goal is to undertake applied research and development with the aim of advancing the scope and the sensitivity of machinery fault detection, diagnosis, prognosis, performance and emissions monitoring. My current researchs investigates: Machine diagnostics and prognostics; Asset integrity and management; Gearbox dynamics and design.

Area of Expertise:

  • Health Monitoring Techniques for Rotating Machinery
  • Finite Element (FE) modelling
  • Condition Monitoring and Aging Management of Structures
  • Vibration based diagnosis for rotating machines
  • Advance Signal Processing

Research Interests

Rotating Equipment; Dynamics and Vibration; Asset integrity and management; Reliability and Failure analysis; Condition Monitoring and Prognosis

Fingerprint Dive into the research topics where Faris Elasha is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 3 Similar Profiles
Bearings (structural) Engineering & Materials Science
Helicopters Engineering & Materials Science
Gears Engineering & Materials Science
Adaptive filters Engineering & Materials Science
transmissions (machine elements) Physics & Astronomy
Acoustic emissions Engineering & Materials Science
Fault detection Engineering & Materials Science
Vibration analysis Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2014 2019

  • 143 Citations
  • 7 h-Index
  • 16 Article
  • 7 Conference proceeding
  • 5 Chapter
  • 1 Paper
6 Citations (Scopus)

A study on helicopter main gearbox planetary bearing fault diagnosis

Zhou, L., Duan, F., Corsar, M., Elasha, F. & Mba, D., Apr 2019, In : Applied Acoustics. 147, p. 4-14 11 p.

Research output: Contribution to journalArticle

Open Access
File
transmissions (machine elements)
helicopters
defects
flight safety
systems health monitoring

Dynamic Modelling of Planetary Gearboxes with Cracked Tooth Using Vibrational Analysis

Manarikkal, I., Elasha, F., Laila, D. S. & Mba, D., 2019, Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Springer Verlag, Vol. 15. p. 240-249 10 p. (Applied Condition Monitoring).

Research output: Chapter in Book/Report/Conference proceedingChapter

Damping
Stiffness
Cracks
Condition monitoring
Modal analysis
1 Citation (Scopus)

Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

Elasha, F., Shanbr, S., Li, X. & Mba, D., 12 Jul 2019, In : Sensors. 19, 14, 17 p., 3092.

Research output: Contribution to journalArticle

Open Access
File
Bearings (structural)
transmissions (machine elements)
machine learning
prognosis
wind turbines
1 Citation (Scopus)
2 Downloads (Pure)

Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Li, X., Elasha, F., Shanbr, S. & Mba, D., 15 Jul 2019, In : Energies. 12, 14, p. 2705 17 p.

Research output: Contribution to journalArticle

Open Access
File
Bearings (structural)
Life Prediction
Supervised Learning
Learning systems
Machine Learning
11 Citations (Scopus)
16 Downloads (Pure)

Detection of Natural Crack in Wind Turbine Gearbox

Shanbr, S., Elasha, F., Elforjani, M. & Teixeira, J. A., Apr 2018, In : Renewable Energy. 118, p. 172-179

Research output: Contribution to journalArticle

Open Access
File
Bearings (structural)
Wind turbines
Cracks
Fault detection
Signal processing

Activities 2016 2018

  • 1 Examination
  • 1 Oral presentation
  • 1 Visiting an external academic institution

Vibration Characteristics of Rolling Element Bearings under Grease Starvation Regime

Faris Elasha (Speaker)
19 Mar 201821 Mar 2018

Activity: Talk or presentationOral presentation

External PhD Degree Examiner

Faris Elasha (Examiner)
5 Jul 2017

Activity: Examination

Cranfield University

Faris Elasha (Visiting researcher)
Jan 20162018

Activity: Visiting an external institutionVisiting an external academic institution