IEC 61164 2004 pdf – Reliability growth – Statistical test and estimation methods

IEC 61164 2004 pdf – Reliability growth – Statistical test and estimation methods.
Many reliability models have been developed for analysing test data. This standard presents one of the most popular growth models, the power law (also known as the AMSAA or the Crow model) in both its continuous and discrete forms. This model is a generalization of the Duane reliability growth model due to Crow [1 ] 1 . Although Bayesian variants of these models exist, they are not presented here. A review of the variety of reliability growth models available for analysing test data can be found in Jewell [2, 3] and Xie [4]. There are fewer documented reports of reliability growth models being used in design. Therefore a reliability growth planning model that is a modification of the power law for use in design and a Bayesian variant of the IBM-Rosner model adapted for design have been introduced. However, these are only given for products operating through continuous time. In general, the choice of a reliability growth model involves a compromise between simplicity and realism. Selection should be made according to the aforementioned criteria such as stage of lifecycle and type of data, as well as by evaluating the validity of the assumptions underpinning a specific model for the context to which it is to be applied. Further details about the assumptions for the models described in this standard are given in Clauses 6 and 7. Note that reliability growth models should not be regarded as infallible nor should they be applied without discretion but used as statistical tools to aid engineering judgement. 6 Reliability growth models used for systems/products in design phase 6.1 Modified power law model for planning of reliability growth in product design phase
This model is used for planning purposes (and not for data analysis), to estimate the number or the magnitude of improvements in the original design to increase its reliability from that initially assessed to its goal value. The assumption of a power law for this model is justified by the fact that the early improvements will be those that will contribute the most to the reliability improvement, that is, the failure modes with the highest probability of occurrence will be addressed first, followed by improvements of lesser and lesser reliability contribution. The actual reliability values achieved in the course of the design are then plotted corresponding to the design time when they were realized and compared to the model. This model is thus used to plan the strategies necessary for reliability improvement of a design during the available time period from the initial design revision until the design is completed and released for production.
Use of fault tree analysis with commercially available software makes assessment of the reliability improvement easy and quick to accomplish and track as the product reliability is automatically calculated based on the changes. After completion of the product design and with the introduction of the product validation phase, the planned reliability growth test may further improve product reliability or uncover failure modes that were not accounted for during analytical evaluations. The final reliability assessment of the completed design can then serve as the reliability goal for the reliability growth testing. An example of practical derivation and application of the planning growth model for reliability improvement in design phase is shown in A.1 .1 . This real life example shows step by step how the model is constructed and how it is used. 6.2 Modified Bayesian IBM-Rosner model for planning reliability growth in design phase 6.2.1 General A model is presented to describe the growth of reliability during the design phase of a repairable item prepared by Quigley and Walls [6] to [8] and is based on a Bayesian adaptation of the IBM-Rosner model [9] which was developed for analysing test data and is described in 7.1 .2. It is assumed a design has been developed to a sufficient level of detail to provide an initial estimate of reliability. It is further assumed that the reliability goal is specified. Modifications to the design will be made with a view to improving reliability until the goal is achieved. The model aims to capture the possible timings of the design modifications. The model assumes that design review and re-assessments result in modifications with the aim of improving reliability and achieving the goal. The rate of growth as measured by advancing the initial reliability to the reliability goal is a function of the removal of aspects of design that contribute to systematic failures.