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OFF540_1

Condition Monitoring and Management

This is the study programme for 2019/2020. It is subject to change.


The course deals with condition monitoring of dynamic machinery and static mechanical equipment by the use of advanced sensors and analysis tools. Furthermore, condition monitoring is discussed as a part of a predictive maintenance strategy (condition based maintenance). The focus is especially directed towards the petroleum industry, but can be generalized towards land based industry.

Learning outcome

The course shall give the student a basic understanding of predictive maintenance management and the basic principles and methods used in condition monitoring of rotating machinery, piping, pressure vessels and load bearing structures.

Contents

Condition monitoring as a part of the maintenance strategy, principles of condition monitoring, establishing a predictive maintenance program. Introduction to monitoring methods using vibration, ultrasonic, thermodynamics, lubrication oil analysis techniques and non-destructive testing (NDT) methods such as penetrant, flux leakage, eddy current, radiography.

Required prerequisite knowledge

None.

Exam

Project work and oral presentation
Weight Duration Marks Aid
Project work in groups1/1 A - F
Oral presentation0/1 Pass - Fail
Project report counts for 100 % of the grade. Both the project report and the presentation (pass/fail) must be passed to pass the course.

Coursework requirements

Guest lectures, 3 lab exercises, company visit
Laboratory exercises (3), company visits, guest lectures.

Course teacher(s)

Course coordinator
Idriss El-Thalji
Head of Department
Tor Henning Hemmingsen

Method of work

Lectures, assignment, laboratory exercises, company visits, guest lectures.

Overlapping courses

Course Reduction (SP)
Condition monitoring and management (MOM350_1) 5

Open to

Master i offshore teknologi, Industriell teknologi og driftsledelse.

Master in Offshore technology, Industrial asset management.

Course assessment

Standard forms and/or discussion.

Literature

Compendium in Condition monitoring and management, selected books and articles, as follows:
1. Fedele, L., From Basic Maintenance to Advanced Maintenance, in Methodologies and techniques for advanced maintenance. 2011. p. 63-112.
2. Barron, R., Condition monitoring: the basics, in Engineering Condition Mointoring: Practice, Methods and Applications. 1996, Pearson Education. p. 6-17.
3. Hitchcock, L., ISO standards for condition monitoring. Pall Corporation. p. 1-12.
4. Buede, D.M., Requirements and defining the design problem, in The engineering design of systems: Models nd methods. 2009, Wiley. p. 151-181.
5. Buede, D.M., Functional architecture development, in The engineering design of systems: Models nd methods. 2009, Wiley. p. 211-217.
6. Buede, D.M., Physical architecture development, in The engineering design of systems: Models nd methods. 2009, Wiley. p. 252-259.
7. Maleque, M.A. and M.S. Salit, Mechanical Failure of Materials, in Materials Selection and Design. 2013, Springer. p. 17-38.
8. Vachtsevanos, G., et al., Sensors and sensing strategies, in Intelligent fault diagnosis and prognosis for engineering systems. 2006, John Wiley & Sons, INC. p. 56-94.
9. White, G.D., Introduction to vibration, in Introduction to machine vibration. 2008, Reliabilityweb.com Press p. 9-29.
10. Sassi, S., B. Badri, and M. Thomas, "TALAF" and "THIKAT" as innovative time domain indicators for tracking BALL bearings in In Compte rendu 24ième séminaire sur la vibration des machines : ACVM. 2006: Calgary, Canada. p. 404-419.
11. White, G.D., The FFT Analyzer, in Introduction to machine vibration. 2008, Reliabilityweb.com Press p. 9-29.
12. Girdhar, P., Machinery fault diagnosis using vibration analysis, in Practical machinery vibration analysis and predictive maintenance, C. Scheffer, Editor. 2004, Elsevier. p. 89-133.
13. Jantunen, E., et al., Problems with using fast Fourier transform for rotating equipment: Is it time for an update?, in 27th international congress of condition monitoring and diagnostic engineering COMADEM. 2014: Brisbane, Australia.
14. Sawalhi, N., Diagnostics, Prognostics and Fault Simulation For Rolling Element Bearings, in School of Mechanical and Manufacturing Engineering. 2007, THE UNIVERSITY OF NEW SOUTH WALES. p. 35-53.
15. Andersson, P. and J. Halme, Rolling contact fatigue and wear fundamentals for rolling bearing diagnostics - state of the art. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2010. 224(4): p. 377-393.
16. Toms, A. and L. Toms, Oil Analysis and Condition Monitoring, in Chemistry and Technology of Lubricants. 2009, Springer. p. 459-495.
17. Dempsey, P., et al., Investigation of bearing fatigue damage life prediction using oil debris monitoring. 2011, NASA. p. 1-11.
18. Hands, G. and T. Armitt, Surface and internal defect detection, in Handbook of condition monitoring, A. Davies, Editor. 1998, Chapman and Hall: London. p. 102-135.
19. Hellier, C.J., Acoustic emission testing, in Handbook of non-destructive evaluation. 2001, McGraw-Hill. p. 10.1-10.39.
20. Miettinen, J. and P. Leinonen. Monitoring of Contaminants in a Grease Lubricated Rolling Bearing by Acoustic Emission in Field Environment. in In: Proceedings of the 2nd COST 516 Tribology Symposium. . 1999. Antwerpen, Mol. : Flemish Institute for Technological Research.
21. Kim, Y.-H., et al., Condition monitoring of low speed bearings: A comparative study of the ultrasound technique versus vibration measure, in WCEAM 2006: Gold Coast, Australia p. 1-10.
22. Bagavathiappan, S., et al., Infrared thermography for condition monitoring - A review. Infrared Physics & Technology, 2013. 60: p. 35-55.
23. El-Thalji, I. and E. Jantunen, A summary of fault modelling and predictive health monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 2015. 60-61: p. 252-272.
24. Sassi, S., B. Badri, and M. Thomas, A Numerical Model to Predict Damaged Bearing Vibrations. Journal of Vibration and Control, 2007. 13(11): p. 1603-1628.
25. Al-Ghamd, A.M. and D. Mba, A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mechanical Systems and Signal Processing, 2006. 20(7): p. 1537-1571.
26. El-Thalji, I. and E. Jantunen, Fault analysis of the wear fault development in rolling bearings. Engineering Failure Analysis, 2015. 57: p. 470-482.
27. Qiu, J., et al., Damage Mechanics Approach for Bearing Lifetime Prognostics. Mechanical Systems and Signal Processing, 2002. 16(5): p. 817-829.


This is the study programme for 2019/2020. It is subject to change.

Sist oppdatert: 16.11.2019

History