Reliability growth management addresses the attainment of the reliability objectives through planning and controlling of the reliability growth process. Reliability growth analysis (RGA) concerns itself with the quantification and assessment of parameters (or metrics) relating to the product's reliability growth over time. The monitoring of the increase of the product's reliability through successive phases in a reliability growth testing program is an important aspect of attaining these goals. Other reliability goals may be associated with failure modes that are safety related. For example, there may be a reliability goal associated with failures resulting in unscheduled maintenance actions and a separate goal associated with those failures causing a mission abort or catastrophic failure. A program may have more than one reliability goal. Reliability goals are generally associated with a reliability growth program. However, in practice, no growth or negative growth may occur. The term "growth" is used since it is assumed that the reliability of the product will increase over time as design changes and fixes are implemented. A reliability growth program is a well-structured process of finding reliability problems by testing, incorporating corrective actions and monitoring the increase of the product's reliability throughout the test phases. Reliability growth is defined as the positive improvement in a reliability metric (or parameter) of a product (component, subsystem or system) over a period of time due to changes in the product's design and/or the manufacturing process. During testing, problem areas are identified and appropriate corrective actions (or redesigns) are taken. In order to identify and correct these deficiencies, the prototypes are often subjected to a rigorous testing program. Because of these deficiencies, the initial reliability of the prototypes may be below the system's reliability goal or requirement. In general, the first prototypes produced during the development of a new complex system will contain design, manufacturing and/or engineering deficiencies. Reliability growth and repairable systems analysis provide methodologies for analyzing data/events that are associated with systems that are part of a stochastic process. Given this dependency, applying a Weibull distribution, for example, is not valid since life data analysis assumes that the events are IID. Events that occur first will affect future failures. There is a dependency between the failures that occur on a repairable system. The time-to-failure of the product after the redesign may follow a distribution that is different than the times-to-failure distribution before the redesign.The time-to-failure of a product after a redesign is dependent on how good or bad the redesign action was.Therefore, the events are dependent and are not identically distributed. A stochastic process is defined as a sequence of inter-dependent random events. For reliability growth and repairable systems analysis, the events that are observed are part of a stochastic process. The age just after the repair is basically the same as it was just before the failure. They are able to have multiple lives as they fail, are repaired and then put back into service. However, this is not the case when dealing with repairable systems that have more than one life. In life data analysis, the unit/component placed on test is assumed to be as-good-as-new. All are mutually independent, which implies that knowing whether or not one occurred makes it neither more nor less probable that the other occurred.Each has the same probability distribution as any of the others.A sequence or collection of random variables is IID if: The Operating Characteristic (OC) curve is generated using Equation 7 in Reference 6.When conducting distribution analysis (and life data analysis), the events that are observed are assumed to be statistically independent and identically distributed (IID). See reference 1, chapter 24, for a more detailed discussion on sequential reliability testing and this blog post for an example. The MTBF that would be observed if an infinite number of units were tested for an infinite amount of time. With a true MTBF equal to the upper test MTBF (θ 0). The producer’s risk is the probability of rejecting an equipment With a true MTBF equal to the lower test MTBF (θ 1). The consumer’s risk is the probability of accepting an equipment The discrimination ratio is one of the standard test plan parameters which establishes the test plan envelope. The test plan will accept an item whose true MTBF is θ 0 with a probability of 1 - α. The test plan will reject an item whose true MTBF is θ 1 with a probability of 1 - β. This tool provides the ability to plan a sequential reliability demonstration test for verification of equipment mean time between failure (MTBF), assuming an exponential failure distribution.
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