Condition Based Reliability, Availability, Maintainability, and Safety (CB-RAMS) model: Improving RAMS predictions by combining condition-monitoring (CM) data with RAMS calculations
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The goal of this paper is to present a practical method for the enhancement of systems Reliability, Availability, Maintainability, and Safety (RAMS) assessments. During the last decades Condition Monitoring (CM) methods have been improved and extensively implement. A method for integration the CM data with RAMS calculations is suggests. Implementing the method as a practical tool and updating RAMS prognostics assessment according to deterioration condition, is demonstrated by examples that emphasize the method’s contribution and advantages. The method is based
on conducting correlations between deterioration stages and Remaining Useful Life. Reliability is continuously updated according to pre-calculated Weibull parameters based on historic deterioration stages accumulated data and concurrent CM findings. The updated assessment represents the real system condition along its deterioration stages. The method enables improved decision making operation and maintenance action thus lower the Life Cycle Cost (LCC). The method named "Condition Based-RAMS" (CB-RAMS). Analyzing systems by CB-RAMS in conjunction with Monte-Carlo Simulation software tool, and CM data, is a practical and efficient method to eliminate surprising dangerous and costly events. The paper introduces the method and improvements that are achievable by implementing it. The contribution of the paper is the applicable detailed procedure to use any sort of CM data to enhance his RAMS predictions. The significance of this paper is that presented approach will enhance the importance of implementing CM on systems to lower LCC and increase safety.
How to Cite
##plugins.themes.bootstrap3.article.details##
Reliability Availability Maintainability and Safety (RAMS), Condition Monitoring (CM), Weibull analysis, Deterioration, Monte Carlo simulation (MCS), Life Cycle Cost (LCC)
Berggren, J. C. (1988). Diagnosing faults in rolling element bearings. Assessing Bearing Condition,4(1), 5–13.
Barringer, P. H. (May, 1998). Life cycle cost and good practices. NPRA Maintenance Conference, San Antonio, Texas.
Barringer, P. H. (2001). Problem of the month, April 2001–Weibull beta slopes for ball bearings. Barringer & Associates. . Retrieved from:http://www.barringer1.com/apr01prb_files/apr01prb.pdf
Bazovsky, I. (1961). Reliability theory and practice. Englewood Cliffs, N.J: Prentice-Hall.
Berry, J. E. (1991). Tracking of rolling element bearing failure stages using vibration signature analysis. Charlotte: Technical Associates of Charlotte.
Berry, J. E. (1997). Tracking of rolling element bearing failure stages using vibration and high frequency enveloping and demodulation spectral techniques. Analysis II - Concentrated Vibration Signature Analysis and Related Condition Monitoring Techniques. 2nd Edition. Charlotte, NC: Technical Associates of Charlotte, P.C.
Berry, J. E. (1999). Technical associates of charlotte: Good vibes about oil analysis. Practicing Oil Analysis Magazine. November/December, 31–39.
Blanks, H. S. (1992). Reliability in procurement and use. Chichester: John Wiley & Sons.
Bond, L. J. et al. (June, 2003). On-line intelligent selfdiagnostic monitoring system for next generation nuclear power plants, Final Project Report for the U.S. Department of Energy. Retrieved from:
http://www.pnl.gov/main/publications/external/technical_reports/pnnl-14304.pdf
Dodson, B. (1994). Weibull analysis. Milwaukee: Amirican Society for Quality_QASA.
Dubi, A. (1986). Monte-Carlo Calculations for Nuclear Reactor. in Y. Ronen (Ed.), Handbook of nuclear reactor calculation. Boca Raton, Fla: CRC Press.
Dubi, A. (1999). Monte Carlo applications in systems engineering. New York: John Wiley & Sons Ltd.
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., & Sun, Y. (2009). A review on degradation models in reliability analysis. Proceeding of the 4th world congress on engineering asset management, Athens, Greece.
Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3). 724–739.
Logistics Engineering Technology Branch, (1998).
Handbook of reliability prediction procedures for mechanical equipment: NSWC-98/LE1, NSWC. West Bethesda, Maryland: Naval Surface Warfare Center, Carderock Division. Retrieved from:
https://www.raytheoneagle.com/asent/downloads/NSWC -98-LE1.pdf
Lycette, B. (2005). Practical considerations in calculating reliability of fielded products. The Journal of the RAC, Second Quarter 2005, 1–6.
O'Connor, P. D. T. (1991). Practical reliability engineering. 3th edition, Chichester: John Wiley & Sons.
Rabinowicz, E. (1981). Why equipment fails. ASLE Bearing Workshop: M.I.T. RAPTOR version 4.0s and RAPTOR 7.
Shalev, D. M. & Tiran, J. (2007). Condition-based fault tree analysis (CB-FTA): A new method for improved fault tree analysis (FTA), reliability and safety calculations. Reliability Engineering & System Safety, 92(9), 1231–1241.
Shreve, D. H. (2010). Rolling-element bearing analysis (reba) techniques and practices. Retrieved from: http://reliabilityweb.com/ee-assets/myuploads/docs/2011/dshreve_doc.pdf
Smith, D. J. (2001). Reliability, maintainability and risk: Practical methods for engineers. 6th edition, Burlington, MA: Elsevier Butterworth-Heinemann.
Stock, D., Vesely, W., & Samanta, P. (1994). Modeling the degradation of nuclear components. New York: Brookhaven National Laboratory.
Sung, K, (1996). How to evaluate fan life. Electrics Cooling. Retrieved from: http://www.electronicscooling.com/1996/05/how-to-evaluate-fan-life/Wesley, J., & Usynin, A. (2008). Current computational trends in equipment prognostics. International Journal of Computational Intelligence Systems, 1(1). 94–102.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.