RelTest – Reliability Engineering
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Reliability across the life cycle.

Reliability engineering connects requirements, risks, testing and field data to support robust development and release decisions.

Explore the five sub-processes
Reliability and its dimensions probability of success, durability, dependability, quality over time and availability to perform a function

Assess failures technically and statistically

Reliability engineering examines the non-functionality of technical products and the causes of failure. It combines statistics and probability theory with mechanical engineering and modern product development.

The goal is not merely to describe failures. Technical problems need to be identified early, assessed systematically and controlled effectively throughout the product life cycle.

Foundation article

Reliability engineering fundamentals

Understand what reliability means, how requirements, metrics and methods connect, and why the product life cycle provides the technical framework.

Explore the fundamentals

Overall process

Sub-processes across the life cycle

The life-cycle phases show the chronological sequence. The five sub-processes structure the technical work across all phases – from defining targets to predicting reliability from field data.

Six unnumbered product life-cycle phases and the five cross-phase sub-processes reliability planning, weak-point analysis, testing, assurance and prediction

Definition

“Reliability is the probability that a product performs its required function without failure for a defined period under specified operating and environmental conditions.”

Five reliability engineering sub-processes

These five topics form the technical core of reliability engineering. Depending on the product and development stage, they can be addressed individually or connected in one integrated process.

01 / Requirements and targets

Reliability planning

Measurable reliability requirements are derived from customer needs, legislation and product strategy. They define targets, responsibilities and a robust verification plan.

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Reliability planning decision-space diagram comparing reliability costs, failure costs and acceptable customer costs

02 / Risks and failure causes

Weak-point analysis

Potential failure causes and critical functions are identified early. FMEA, FTA, reviews and field experience help prioritise risks and target improvements.

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Failure-rate bathtub curve with reduced early and random failures and a wear-out phase shifted to a later time
The arrows show different effects: early and random failures are reduced, while wear-out failures are shifted to later operating times.

03 / Testing and lifetime

Reliability testing

Tests assess function and lifetime under representative loads. Load profiles, samples and test durations are selected to generate robust evidence efficiently.

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Comparison of worst-case, use-specific and synthetic load profiles for reliability testing

04 / Verification and release

Reliability assurance

Calculations, models, test results and field data are combined in a traceable verification case, supporting a robust assessment and documented release decision.

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Component failure probabilities are combined through a series system to obtain the system failure probability
This is a non-redundant series system: failure of any component causes system failure. The system failure probability therefore exceeds each component probability; redundant architectures may behave differently.

05 / Data and prediction

Reliability prediction

Test and field data are analysed statistically and translated into lifetime models. Predictions quantify failure behaviour, remaining life and uncertainty for technical decisions.

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Reliability prediction from test and field data using a lifetime model and three different operating-time profiles
Test and field data feed the lifetime model; the comparison chart shows short, medium and long predicted operating times.

Separate methodological focus

Design of Experiments complements the reliability process.

DoE is not a sixth sub-process. Statistical experimental design complements testing and data analysis when multiple factors, interactions and robust settings need to be understood with as few experimental runs as practical.

Explore the DoE fundamentals
Simplified DoE graphic: temperature and speed are tested in four combinations, non-parallel lines reveal an interaction
A structured experimental design varies temperature and speed together. Non-parallel response lines make the interaction visible.

Clarify your project's technical question

In an initial discussion, we identify the relevant sub-process and the right methodological depth for your decision.

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