but, some are useful
As said by George E. P. Box. He was talking about statistical modeling and the basic idea behind actually doing the modeling.
We want to make decisions.
In reliability engineering we have, at times, a lot of life data. Construction models that describe the chance of failure over time is useful to
- understand the changing rate of failures over time
- forecast future failures
- predict performance of future products
The simplification caused by modeling comes with some risks. The model is certainly wrong versus reality, yet if close enough is still useful. It is when we ignore these risks that we make poor decisions.
MTBF is just a poor model representing failure rate over time. Using only MTBF without other information assumes the hazard function is constant. Always check this assumption as it is rarely true.
The notion that failure rates double with an increase of 10°C is based on the Arrhenius reaction rate equation with an activation of about 0.7eV. Two assumptions to check here
- Is the failure mechanism a chemical reaction and well described by
the Arrhenius equation?
- If so, is the activation energy really
There are other assumptions to check, models to validate, failure mechanism to understand, and more to learn. Reliability engineering is a broad and expanding field and our knowledge should continue to expand also.
Consider the models and assumptions you are using today — you most likely are using many models to assist your work in reliability engineering.
Are they really useful?