Reliability prediction for electronic components: Difference between revisions

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A [https://en.wikipedia.org/wiki/Prediction prediction] of reliability is an important element in the process of selecting equipment for use by [[telecommunications]] [[service providers]] and other buyers of [[electronic equipment]]. Reliability is a measure of the [[frequency]] of equipment failures as a function of time. [[Reliability]] has a major impact on maintenance and repair costs and on the continuity of service.<ref>Terry Donovan, Senior Systems Engineer Telcordia Technologies. Member of Optical Society of America, IEEE, "Automated Reliability Prediction, SR-332, Issue 3", January 2011; "Automated Reliability Prediction (ARPP), FD-ARPP-01, Issue 11", January 2011</ref>

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Every product has a [[failure rate]], λ which is the number of units failing per unit time. This failure rate changes throughout the life of the product. It is the [[manufacturer]]’s aim to ensure that product in the “infant mortality period” does not get to the [[customer]]. This leaves a product with a useful life period during which failures occur randomly i.e., λ is constant, and finally a wear-out period, usually beyond the products useful life, where λ is increasing.

A [https://en.wikipedia.org/wiki/Prediction [prediction]] of reliability is an important element in the process of selecting equipment for use by [[telecommunications]] [[service providers]] and other buyers of [[electronic equipment]], and it is essential during the design stage of engineering systems life cycle.<ref>EPSMA, “Guidelines to Understanding Reliability Predictions”, EPSMA, 2005</ref> Reliability is a measure of the [[frequency]] of equipment failures as a function of time. [[wikt:reliability|Reliability]] has a major impact on maintenance and repair costs and on the continuity of service.<ref>Terry Donovan, Senior Systems Engineer Telcordia Technologies. Member of Optical Society of America, IEEE, "Automated Reliability Prediction, SR-332, Issue 3", January 2011; "Automated Reliability Prediction (ARPP), FD-ARPP-01, Issue 11", January 2011</ref>

Every product has a [[failure rate]], λ which is the number of units failing per unit time. This failure rate changes throughout the life of the product. It is the [[manufacturer]]’s's aim to ensure that product in the “infant mortality period” does not get to the [[customer]]. This leaves a product with a useful life period during which failures occur randomly i.e., λ is constant, and finally a wear-out period, usually beyond the productsproduct's useful life, where λ is increasing.

==Contents==

* Definition of Reliability

* MTBF and MTTF

* Importance of Reliability prediction

* References

== Definition of reliability ==

A practical definition of reliability is “the probability that a piece of equipment operating under specified conditions shall perform satisfactorily for a given period of time”. The reliability is a number between 0 and 1 respectively.

== MTBF and MTTF ==

[[MTBF]] (mean operating time between failures) applies to equipment that is going to be repaired and returned to service, [[MTTF]] (mean time to failure) applies to parts that will be thrown away on failing. During the ‘useful life period’ assuming a constant failure rate, MTBF is the inverse of the failure rate and the terms can be used [[Interchangeable|interchangeably]].

== Importance of reliability prediction ==

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:# ''Unit'': Any assembly of devices. This may include, but is not limited to, circuit packs, modules, plug-in units, racks, power supplies, and ancillary equipment. Unless otherwise dictated by maintenance considerations, a unit will usually be the lowest level of replaceable assemblies/devices. The RPP is aimed primarily at reliability prediction of units.

:# ''Serial System'': Any assembly of units for which the failure of any single unit will cause a failure of the system.

== Data-driven reliability predictions ==

Data-driven models for reliability prediction utilise data acquired from tests to failure on electronic components by establishing relationships between the different variables presented in the data. As such relationships can be complex, data-driven models often require computations in high dimensions, which means that a large dataset is needed to optimize the output of the model.<ref>{{cite conference |last1=Ghrabli |first1=Mehdi|last2=Bouarroudj |first2=Mounira | author3=Chamoin, Ludovic|author4=Aldea, Emanuel |date=2024 |title=Hybrid modeling for remaining useful life prediction in power module prognosis |conference=2024 25th International Conference on Thermal Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)|___location=Catania, Italy |publisher=IEEE|doi=10.1109/EuroSimE60745.2024.10491493 }}</ref>

== Physics-based reliability predictions ==

Physics based reliability predictions use physical equations and formulae to determine failure. This approach requires precise knowledge of the degradation process and the physical properties to ensure accuracy. These models often utilise numerical simulations to infer the quantities needed by the model.<ref>{{ cite journal | title=Physics-informed Markov chains for remaining useful life prediction of wire bonds in power electronic modules | journal=Microelectronics Reliability | year=2025 | last1=Ghrabli | author2=Bouarroudj, Mounira | author3=Chamoin, Ludovic|author4=Aldea, Emanuel | volume=167 | pages=1–12 | first1=Mehdi | article-number=115644 | doi=10.1016/j.microrel.2025.115644| bibcode=2025MiRe..16715644G | doi-access=free }}</ref>

== References ==