Prognostics and Health Management (PHM) methods are incorporated into systems for the purpose of avoiding unanticipated failures that can impact system safety, result in additional life cycle cost, and/or adversely affect the availability of the system. Availability is the probability that a system will be able to function when called upon to do so. A vailability depends on the system’ s reliability (how often it fails) and its maintainability (how efficiently and frequently it is pro-actively maintained, and how quickly it can be repaired and restored to operation when it does fail). Availability is directly impacted by the success of PHM. Increasingly, customers of critical systems are entering into “availability contracts” in which the customer either buys the availability of the system (rather than actually purchasing the system itself) or the amount that the system developer/manufacturer/supplier is paid for the system is a function of the availability achieved by the customer. Predicting availability based on known or predicted system reliability, operational parameters, logistics, etc., is relatively straightforward and can be accomplished using existing methods. However, while determining the availability that results from a set of events is straightforward, determining the events that result in a desired availability is not, and prediction of a system’s attributes to meet an availability requirement can only be accomplished using “brute force” search-based methods that are not general and become quickly impractical for real systems and when uncertainties are introduced. This paper presents a “design for availability” approach that starts with an availability requirement and uses it to predict the required logistics, design and operation parameters. The method is general and can be applied when the inputs to the problem are uncertain (even the availability requirement can be a probability distribution). The method is demonstrated on several examples with and without PHM.
How to Cite
electronics PHM, availability, Cost Modeling
(Bazargan and McGrath, 2003) M. Bazargan and R. N.
McGrath, Discrete event simulation to improve aircraft availability and maintainability, in Proceedings of the Annual Reliability and Maintainability Symposium, 2003, pp. 63-67.
(Beanum, 2006) R. L. Beanum, Performance Based logistics and contractor support methods, in Proceedings of the IEEE Systems Readiness Technology Conference (AUTOTESTCON), September 2006.
(Castro and Cavalca, 2006) H. F. Castro and K. L. Cavalca, A vailability optimization with genetic algorithm, Reliability Engineering & System Safety, vol. 91, no. 4, pp. 413-420, April 2006.
(Federal A viation Administration, 2001) Federal A viation Administration, Investment Analysis Benefit Guidelines: Quantifying Flight Efficiency Benefits, V ersion 3.0, Investment Analysis and Operations Research Group, June 2001.
(Feldman et al., 2009) K. Feldman, T. Jazouli, and P. Sandborn, A methodology for determining the return on investment associated with prognostics and health management, IEEE Trans. on Reliability, vol. 58, no. 2, pp. 305-316, June 2009.
(Henkle et al., 2002) A. Henkle, C. Lindsey, and M. Bernson, Southwest Airlines: A review of the operational and cultural aspects of Southwest Airlines, Operations Management Course Presentation, Sloan School of Management, Summer 2002.
(Hyman, 2009) W . A. Hyman, Performance-based contracting for maintenance, NCHRP Synthesis 389, ISSN:0547-5570, 2009.
(Janakiraman et al., 2004) G. Janakiraman, J. R. Santos, and Y. Turner, Automated system design for availability, in Proceedings of the International Conference on Dependable Systems and Networks (DSN'04), 2004.
(Juan et al., 2009) A. A. Juan, A. Monteforte, A. Ferrer, C. Serrat, and J. Faulin, Applications of discrete- event simulation to reliability and availability assessment in civil engineering structures, in Proceedings of the Winter Simulation Conference, pp. 2759-2767, 2009.
(Kececioglu, 1995) D. Kececioglu, Maintainability, Availability, Operational Readiness, Engineering Handbook, Volume 1, Prentice Hall PTR, 1995.
(Kumar et al., 2000) D. Kumar, J. Crocker, J. Knezevic, and M. El-Haram, Reliability Maintenance and Logistic Support: A Life Cycle Approach, Springer, 2000.
(Macheret, 2005) Y . Macheret, P . Koehn, and D. Sparrow, Improving reliability and operationa availability of military systems, in Proceedings of
the IEEE Aerospace Conference, 2005.
(Ng et al., 2009) I. C. L. Ng, R. Maull, and N. Yip, Outcome-Based Contracts as a driver for Systems thinking and Service-Dominant Logic in Service Science: Evidence from the Defence industry, European Management Journal, vol. 27, no.6, pp. 377-387, December 2009.
(Oliveto, 2001) F. E. Oliveto, An optimal sparing
model for the operational availability to approach the inherent availability, in Proceedings of the Annual Reliability and Maintainability Symposium, 2001, pp. 252-257.
(Raffo and Setamanit, 2003) D. M. Raffo and S. Setamanit, Supporting software process decisions using bi-directional simulation, International Journal of Software Engineering and Knowledge Engineering (IJSEKE), vol. 13, no. 5, pp. 513-530, 2003.
(Reynolds and McKeown, 2007) A. P. Reynolds and G. P . McKeown, Construction of factory schedules using reverse simulation, European Journal of Operational Research, vol. 179, no. 3, pp. 656- 676, June 2007.
(Sandborn and Wilkinson, 2007) P. A. Sandborn and C. Wilkinson, A maintenance planning and business case development model for the application of prognostics and health management (PHM) to electronic systems, Microelectronics Reliability, vol. 47, no. 12, pp. 1889-1901, December 2007.
(Scanff et al., 2007) E. Scanff, K. Feldman, S. Ghelam, P. Sandborn, M. Glade, and B. Foucher, Life cycle cost estimation of using prognostic health management for helicopter avionics, Microelectronic Reliability, vol. 47, no. 12, pp. 1857-1864, December 2007.
(Yeh and Chang, 2007) R. H. Yeh, W. L. Chang, Optimal threshold value of failure-rate for leased products with preventive maintenance actions, Mathematical and Computer Modeling, vol. 46, pp.730-737, 2007.
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.