ENBIS Spring Meeting 2017

28 – 30 May 2017; Monastery of Schlägl in Upper Austria Abstract submission: 11 November 2016 – 5 March 2017

Reliability Growth Monitoring in Product Validation

30 May 2017, 12:35 – 12:55

Abstract

Submitted by
Stefan Müllner
Authors
Stefan Müllner (CIS Consulting in Industrial Statistics GmbH)
Abstract
In the development of complex products reliability is becoming a more and more important aspect. From the engineering perspective the aim is to increase the reliability of a complex system by testing it in various situations which cover the range of customer use appropriately. During the tests the failures occurring are recorded and monitored over the course of time. In a successful test programme, the number of failures over time decreases due to corrections implemented into the system, and thus reliability growth can be monitored. Developing tests in order to detect failure causes and modes and verify system reliability is an important objective in reliability engineering.

We will present a monitoring algorithm which allows the user to assess reliability and make predictions at arbitrary time points. Furthermore the algorithm determines whether a previously specified reliability target will be reached, and proposes actions in case the reliability target is unlikely to be attained.

The monitoring algorithm applies the Crow-AMSAA reliability growth tracking model. Estimations are made by using the Maximum Likelihood method, and confidence bounds for the model are computed by applying the Fisher matrix approach. The reliability growth monitoring algorithm may be applied to both exact failure time data and grouped data, in which case the failure counts in time intervals are known only. The algorithm was developed such that it can be used for data in which no information of failure modes is available, and incorporates the information resulting in more detailed evaluations otherwise.

Application of the algorithm is shown in several examples with real-life datasets. Analyses and predictions are performed retrospectively and the goodness-of-fit of the Crow-AMSAA model is evaluated.

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