Condition-Based Diagnostic Approach for Predicting the Maintenance Requirements of Machinery

C. I. UGECHI, E. A. OGBONNAYA, M. T. LILLY, S. O. T. OGAJI, S. D. PROBERT

Department of Marine Engineering, Rivers State University of Science and Technology,Port Harcourt, Nigeria.
Department of Mechanical Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria.
School of Engineering, Cranfield University, Bedfordshire, U K.

DOI: 10.4236/eng.2009.13021                

Abstract

Wise maintenance-procedures are essential for achieving high industrial productivities and low energy expenditure. A major part of the energy used in any production process is expended during the maintenance of the employed equipment. To ensure plant reliability and equipment availability, a condition-based maintenance policy has been developed in this investigation. In particular, this project explored the use of vibration parameters in the diagnosis of equipment failure. A computer-based diagnostic tool employing an artificial neural-network (ANN) was developed to analyse the ensuing machinery faults, their causes and consequences. For various categories of this type of machinery, a vibration-severity chart (ISO 12372 / BS 4675: 1971) appropriately colour coded according to defined mechanical faults, was used in training of the ANN. The model was validated using data obtained from a centrifugal pump on full load and fed into the program written in Visual Basic. The results revealed that, for centrifugal pumps within 15 to 300kw power range, vibration-velocity amplitude of between 0.9 and 2.7mm/s was within acceptable limits. When the values rose to between 2.8 and 7.0mm/s, closer monitoring and improved understanding of the equipment condition was needed. The evolved diagnostic and prognostic model is applicable for other rotary equipment that is used within the same power limits.

Keywords

Condition Based, Diagnostic Model, Predictive Maintenance, Machinery, Centrifugal Pumps

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	L. R. Higgins, “Maintenance engineering handbook,” McGraw-Hill Book Company, New York, USA, pp. 22– 35, 1995.
[2]	E. A. Ogbonnaya, “Condition monitoring of a diesel engine for electricity generation,” M. Tech. Thesis, Department of Marine Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria, p. 68, 1998.
[3]	L. R. Contreras, C. Modi, and A. Pennathur, “Integrating simulation modelling and equipment condition diagnostics for predictive maintenance strategies–A case study,” Proceedings of the 2002 Winter Simulation Conference, University of Texas, El Paso, USA, pp. 1–7, 2002.
[4]	M, Salva, R. Hernandez, and C. Sanz, “Proposed of a method for implementing infrared-scanning inspection programs in merchant vessels,” Marine Technology and Sname News, Vol. 41, No. 1, pp. 1–6, 2004.
[5]	J. M.Moubray, “Maintenance management: A new paradigm, strategic technologies,” Inc., Aladon Ltd, UK, pp. 7 –11, 2000. [Online] Available: http://www.maintenanceresources.com/RCM/Maintparadigm, July 29, 2006.
[6]	A. Veltkamp, “Vibration introduction course: SKF condition monitoring, in computerized maintenance management system and enterprise resource planning,” Nigerian Society of Engineers, Lagos, Nigeria, pp. 1.1–4.5, 2001.
[7]	S. Jeff, PDM, “Secrets revealed: How to improve your PDM program or start one from scratch,” 1st Edition, Allied Reliability, Inc., Tulsa, Oklohoma, [Online] Available: http://www.alliedreliability.com, pp. 4–35, June 6, 2006.
[8]	E. A. Ogbonnaya, “Modelling vibration-Basic faults in rotor shafts of a gas turbine,” PhD Thesis, Department of Marine Engineering, Rivers State University of Science and Technology, Port Harcourt, Nigeria, p. 251, 2004.
[9]	B. E. Okah-Avae, “The science of industrial machinery and systems maintenance,” Spectrum Books Ltd, Lagos, Nigeria, pp. 19–105, 1996.
[10]	W. P. P. Ralph, “Maintenance management and control,” in Handbook of Industrial Engineering: Technology and Operations Management, 3rd Ed., Inc. New York, USA, pp. 1611–1615, 2001.
[11]	IRD Mechanalysis, “Vibration monitoring and analysis handbook,” IRD Mechanalysis, Inc., Columbus, USA, pp. 1.7–5.7, 1993.
[12]	J. T. Broch, “Mechanical vibration and shock measurement,” 2nd Ed., K. Larsen & Son A/S, Denmark, pp. 197– 199, 1980.
[13]	K. A. Oladejo and A. O. Oke, “Condition monitoring of a centrifugal pump,” in Pump Maintenance, Arcedem Se- minar Paper, Ibadan, Nigeria, pp. 6–10, 2005.
[14]	E. P. Agbese and A. Mohammed, “Artificial neural network and their applications,” Proceedings of First National Conference of the Department of Mechanical Engineering on Manufacturing Technology and Engineering in a Developing Economy, University of Uyo, Nigeria, May 2–3, pp 51–55, 2001.
[15]	J. S. Carlton, C. G. Holland, M. J. Newbury, D. Rhoden, and D. A. Triner, “Recent operational experience and research relating to marine machinery condition monitoring,” Trans IMarE, Vol. 109, No. 1, pp. 75–95, 1997.
[16]	E. A. Ogbonnaya, K. D. H. Bob-Mannual, I. E. Douglas, and H. I. Hart, “Application of artificial neural networks to gas turbine rotor shaft faults diagnosis,” Journal of Science and Technology Research, Vol. 3, No. 1, pp. 12–15, 2004.
[17]	K. Farhad, A. P. Mehmet, and M. Ehsan, “A new generation motion-control system for twin-hull vessels using a neural optimal controller,” Marine Technology and Sname News, Vol. 40, No. 3, pp. 168–180, 2003.