Wear Out Phase Prediction

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Wear Out Phase Prediction

It would be nice to start a discussion about the wear out phase of the electronic lifecycle.

The wear-out phase is often modeled with the normal distribution (RIAC) but I haven′t seen any good examples of mean or standard deviations used in such models.

Anecdotal evidence seems to point to new electronics lasting three to ten years, CRT TVs lasting decades and radios lasting as long as forty years.

Has anyone seen examples of wear out data? What kind of relationship exists between equipment complexity and wear out? I would assume environment variables could be applied to the wear out phase but is there any evidence of this (e.g. do electronics usually outlast vehicles or vise versa)?

Re: Wear Out Phase Prediction
There is allready a topic on this usbject... but in French!

End of life modeling. For me the normal distribution is not a proper law to model end of life even if it may be used in some cases. The mostly used law is the Weibull law, because it can be adjusted with the shape factor to take into account various types of phenomenon. The end of life for mechanical fatigue shall not be modelized the same way as true wearing (comsumption of a potential, like for a tyre engravings). It exists many model for end of life prediction, analytical (Engelmaier) or simulation (Criteria, CALCE PWA). They can not replace reliability prediction. On the contrary, in some way, they assess the duration during wich the relaibility prediction applies. Some people (at CALCE for example) seems to have not understand this point yet because they are still critisizing reliability prediction methods. Reliability prediction and end-of-life prediction are not two possible options: both are needed.


If a design is done with component very sensible to aging (for example big component like BGA with more than 1000 pin in) in harsh environment (for example big thermal cycle), early systematic failures may occur.

For wear out data, it is probably usefull to search in the field of analytical methods (Engelmaier) or simulation methods (Criteria, CALCE).

There is many possible reliationship between complexity and aging :

- Complex component (like big BGA) are more sensible to thermal cycling because of their size.

- The life limit is the limit of the weakest part (this statement is FALSE for failure rate, of course!). The more complex, the more likely to have a weak point...

And of course environments do accelerate aging.

Points of view on this topic may vary a lot upon the technical domain: Automotive mainly look at early failure in the waranty time. Energy production mainly works on the end of life. The logistician in his shop only know constant failure rate...

I hope this will help and that others will give their point of view!