A "DESIGN FOR AVAILABILITY" METHODOLOGY FOR SYSTEMS DESIGN AND SUPPORT
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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 a 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 is paid 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 several methods and many existing tools. Unfortunately in these approaches availability is an output of the analysis. The prediction of system's parameters (i.e., reliability, operational parameters, and/or logistics management) to meet an availability requirement is difficult and cannot be generally done using today's existing methods. While determining the availability that results from a set of events is straightforward, determining the events that result in a desired availability is not. This dissertation presents a "design for availability" methodology that starts with an availability requirement and uses it to predict the required design, logistics and operations parameters. The method is general and can be applied when the inputs to the problem are uncertain (even the availability requirement can be represented as a probability distribution). The method has been demonstrated on several examples with and without PHM.