science

Reliability prediction for electronic components

Reliability prediction for electronic components is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Reliability prediction for electronic components rather than just read about it. In short: A 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. Reliability is a measure of the frequency of equipment failures as a function of time.

Key takeaways

Reliability prediction for electronic components belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Reliability prediction for electronic components to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Reliability prediction for electronic components from memory before moving on to harder problems.

Reference excerpt

A 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. 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. 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 product's useful life, where λ is increasing.

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 interchangeably.

Importance of reliability prediction Reliability predictions:

Help assess the effect of product reliability on the maintenance activity and on the quantity of spare units required for acceptable field performance of any particular system. For example, predictions of the frequency of unit level maintenance actions can be obtained. Reliability prediction can be used to size spare populations. Provide necessary input to system-level reliability models. System-level reliability models can subsequently be used to predict, for example, frequency of system outages in steady-state, frequency of system outages during early life, expected downtime per year, and system availability. Provide necessary input to unit and system-level life cycle cost analyses. Life cycle cost studies determine the cost of a product over its entire life. Therefore, how often a unit will have to be replaced needs to be known. Inputs to this process include unit and system failure rates. This includes how often units and systems fail during the first year of operation as well as in later years. Assist in deciding which product to purchase from a list of competing products. As a result, it is essential that reliability predictions be based on a common procedure. Can be used to set factory test standards for products requiring a reliability test. Reliability predictions help determine how often the system should fail. Are needed as input to the analysis of complex systems such as switching systems and digital cross-connect systems. It is necessary to know how often different parts of the system are going to fail even for redundant components. Can be used in design trade-off studies. For example, a supplier could look at a design with many simple devices and compare it to a design with fewer devices that are newer but more complex. The unit with fewer devices is usually more reliable. Can be used to set achievable in-service performance standards against which to judge actual performance and stimulate action. The telecommunications industry has devoted much time over the years to concentrate on developing reliability models for electronic equipment. One such tool is the automated reliability prediction procedure (ARPP), which is an Excel-spreadsheet software tool that automates the reliability prediction procedures in SR-332, Reliability prediction procedure for electronic equipment. Archived 2011-07-16 at the Wayback Machine FD-ARPP-01 provides suppliers and manufacturers with a tool for making reliability prediction procedure (RPP) calculations. It also provides a means for understanding RPP calculations through the capability of interactive examples provided by the user. The RPP views electronic systems as hierarchical assemblies. Systems are constructed from units that, in turn, are constructed from devices. The methods presented predict reliability at these three hierarchical levels:

Device: A basic component (or part) 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.

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.

References

Worked examples

Example 1 — a first encounter with Reliability prediction for electronic components

Start with the simplest possible case. Write down what Reliability prediction for electronic components claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Reliability prediction for electronic components before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Reliability prediction for electronic components ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Reliability prediction for electronic components

In research: Reliability prediction for electronic components appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Reliability prediction for electronic components in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Reliability prediction for electronic components is common in secondary-school and first-year university syllabi. It links to neighbouring topics Electrical components, so understanding it makes those chapters shorter.
In everyday life: Look for Reliability prediction for electronic components outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Reliability prediction for electronic components in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Reliability prediction for electronic components means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Reliability prediction for electronic components out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Reliability prediction for electronic components in simple terms?

Why does Reliability prediction for electronic components matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Reliability prediction for electronic components?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Reliability prediction for electronic components.