Annual Conference of the Prognostics and Health Management Society, 2010

A Design for Availability Approach for Use with PHM

Taoufik Jazouli
Peter Sandborn

Center for Advanced Life Cycle Engineering (CALCE)
Department of Mechanical Engineering
University of Maryland
College Park, MD 20742


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. Availability 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 systems 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.

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