A "DESIGN FOR AVAILABILITY" METHODOLOGY FOR SYSTEMS DESIGN AND SUPPORT

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

ELECTRONIC PROGNOSTICS AND HEALTH MANAGEMENT: A RETURN ON INVESTMENT ANALYSIS

Prognostics and Health Management (PHM) provides the potential to lower sustainment costs, to improve maintenance decision-making, and to provide product usage feedback into the product design and validation process. A case analysis was developed using a discrete event simulation to determine the benefits and the potential cost avoidance resulting from the use of PHM in avionics. The model allows for variability in implementation costs, operational profile, false alarms, random failure rates, and system composition to enable a comprehensive calculation of the Return on Investment (ROI) in support of acquisition decision making. The case analysis compared the life cycle costs using unscheduled maintenance to the life cycle costs using two types of PHM approaches

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We develop and estimate optimal age replacement policies for devices whose age is measured in multiple time scales. For example, the age of a jet engine can be measured in chronological time, the number of flight hours, and the number of landings. Under a single-scale age replacement policy, a device is replaced at age or upon failure, whichever occurs first. We show that a natural generalization to k > 2 scales is to replace non-failed devices when their usage path crosses the boundary of a k-dimensional region M, where M is a lower set with respect to the matrix partial order. For lifetimes measured in two scales, we consider two contexts. In the first, devices age along linear usage paths. For this case, we generalize the single-scale long-run average cost and estimate optimal two-scale policies. We show these policies are strongly consistent estimators of the true optimal policies under mild conditions, and study small- sample behavior using simulation. For the second context, in which device usage paths are unknown, we use two-dimensional renewal theory to derive the long-run average cost of a policy. We give examples in both settings and note that these ideas generalize to more than two scales.http://archive.org/details/agereplacementpo1094532940NANAU.S. Air Force (U.S.A.F.) author.Approved for public release; distribution is unlimited