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8. Assessing Product Reliability
8.2. Assumptions/Prerequisites


How do you choose an appropriate life distribution model?

Choose models that make sense, fit the data and, hopefully, have a plausible theoretical justification Life distribution models are chosen for one or more of the following three reasons:
  1. There is a physical/statistical argument that theoretically matches a failure mechanism to a life distribution model
  2. A particular model has previously been used successfully for the same or a similar failure mechanism 
  3. A convenient model provides a good empirical fit to all the failure data
Whatever method is used to choose a model, the model should
  • "make sense" - for example, don't use an exponential model with a constant failure rate to model a "wear out" failure mechanism
  • pass visual and statistical tests for fitting the data.
Models like the lognormal and the Weibull are so flexible that it is not uncommon for both to fit a small set of failure data equally well. Yet, especially when projecting via acceleration models to a use condition far removed from the test data, these two models may predict failure rates that differ by orders of magnitude. That is why it is more than an academic exercise to try to find a theoretical justification for using a particular distribution.
There are several useful theoretical arguments to help guide the choice of a model We will consider three well-known arguments of this type:  Note that physical/statistical arguments for choosing a life distribution model are typically based on individual failure modes.
For some questions, an "empirical" distribution- free approach can be used The Kaplan-Meier technique can be used when it is appropriate to just "let the data points speak for themselves" without making any model assumptions. However, you generally need a considerable amount of data for this approach to be useful, and acceleration modeling is much more difficult.
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