Reliability engineering across the asset lifecycle: concepts, availability, maintainability, failures, methods, metrics, and technical decisions.

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Reliability engineering is the discipline that structures requirements, analyses, and decisions so that systems, equipment, facilities, and services perform their required functions for a defined period and under the expected conditions of use. Rather than addressing failure only after it occurs, the approach seeks to understand how performance can degrade, which failure mechanisms are relevant, and which design, operation, and maintenance decisions reduce risk throughout the lifecycle.

In practice, reliability is not synonymous with maintenance. Maintenance acts on existing assets and on restoring or preserving their functions; reliability engineering also works before operation, defining requirements, analyzing architectures, evaluating redundancy, identifying failure modes, establishing test criteria, and producing evidence for investment, operation, and renewal decisions.

This perspective is particularly important in critical systems, where downtime can affect production, safety, operational continuity, quality, revenue, or regulatory obligations. In these environments, reliability, availability, and maintainability need to be treated as engineering requirements, not merely as historical indicators.

What is reliability engineering?

Reliability engineering applies quantitative and qualitative methods to understand the ability of an item to perform a required function over a given interval and under specified conditions. The object of analysis may be a component, equipment item, subsystem, facility, process, software, or integrated system.

IEC 60300-1:2024 addresses dependability from technical, financial, and business perspectives and reinforces its management throughout the lifecycle. In this context, reliability is related to other attributes, especially availability, maintainability, and supportability.

This changes the question from “how many failures did we have?” to engineering questions such as:

  • which function must be preserved and under what conditions;
  • what level of availability the process requires;
  • which failure modes can prevent the function from being fulfilled;
  • which failures can be detected before functional loss;
  • which architectures and redundancies are justified;
  • how long is acceptable to restore the function;
  • which resources, spare parts, procedures, and competencies are required;
  • which evidence demonstrates that the requirements were achieved.

For business applications, the analysis must be connected to operational objectives. A highly reliable facility that is economically disproportionate to the risk it mitigates may be technically sophisticated and still be inappropriate for the business.

Reliability is an engineering requirement before it becomes a maintenance metric. Architecture, redundancy, diagnostics, recovery capability, and test criteria must be defined according to risk and the required function.

Reliability and Availability Engineering →

Reliability, availability, and maintainability: what is the difference?

The three concepts are related, but they answer different questions.

Reliability is associated with the ability to perform a function without failure over an interval and under established conditions. It is influenced by design, component selection, environment, loading, manufacturing quality, installation, operation, and degradation mechanisms.

Maintainability is related to the ease and time required to maintain or restore an item. Accessibility, modularity, diagnostics, procedures, tools, training, and spare-parts availability directly influence this attribute.

Availability expresses the ability of the item to be in a condition to perform the function when required. Therefore, it results not only from failure frequency, but also from the time required to restore the function and from the existing support structure.

A system may have highly reliable components and still have low availability if recovery is slow, diagnostic capability is poor, or critical spares are unavailable. Likewise, equipment with relatively frequent failures may maintain acceptable availability when it has fault-tolerant architecture and rapid restoration.

Reliability engineering is not just MTBF calculation

MTBF has become one of the best-known indicators, but using it in isolation can lead to poor interpretations. The indicator represents mean time between failures under conditions and populations compatible with how the data were obtained; by itself it does not show the failure distribution, the mechanisms involved, the severity of effects, or the probability of failure at a specific time.

Commonly used metrics include:

  • MTBF — Mean Time Between Failures: typically applied to repairable items;
  • MTTF — Mean Time To Failure: used to characterize time to failure, often for non-repairable items;
  • MTTR — Mean Time To Repair/Restore: measures the time associated with recovery, according to the definition adopted;
  • failure rate: occurrence frequency relative to exposure;
  • reliability R(t): probability of functional survival up to a given time;
  • availability: proportion or probability that the system is able to perform when required;
  • downtime: unavailability time, which should be segmented by cause and nature;
  • failure recurrence: useful for identifying root causes that have not been eliminated.

Metrics need context: system boundary, period, population, failure criteria, operating conditions, and data-treatment rules.

MTBF, MTTR, and availability should not be interpreted outside the boundary and conditions of the analysis. Data without consistent taxonomy, exposure, and failure criteria can create false precision.

Engineering Asset Management →

Main methods used

Reliability engineering combines methods. No single tool is suitable for every problem.

FMEA and FMECA

Failure Modes and Effects Analysis — FMEA identifies ways in which an item or process can fail, their effects, and causes, allowing actions to be prioritized. When the assessment formally incorporates criticality, the approach may be treated as FMECA. IEC 60812:2018 provides a generic structure applicable to hardware, software, processes, and interfaces.

FMEA is particularly valuable when performed before failure is embedded in the design or process. It can also support maintenance, modifications, analysis of existing systems, and review of control strategies.

RAM analysis

RAM — Reliability, Availability and Maintainability analysis integrates reliability, availability, and maintainability to assess whether a function can remain available throughout a mission or operating horizon. In practice, it combines failure behavior with recovery capability: a system can have reliable components and still have low availability when restoration is slow, just as it can tolerate frequent failures when the architecture has effective redundancy and rapid recovery.

The first step is not to choose a formula, but to define the required function, the system boundary, operating conditions, and the success criterion. This logic is consistent with the dependability terminology of ABNT NBR 5462: reliability, maintainability, availability, and maintenance support are related attributes, but they are not interchangeable. If the analyzed function is “maintain electrical supply to a critical load,” for example, the boundary must include every element capable of interrupting that function — not only the equipment considered primary.

A RAM analysis can compare series and parallel architectures, N+1 or 2N redundancies, repair times, spare-parts inventories, support contracts, and design alternatives. The most useful result is not an isolated availability percentage, but the identification of the largest contributors to failure and unavailability, making it possible to test which interventions actually change system performance.

For this reason, RAM is particularly valuable before CAPEX decisions, during Design Review, and when establishing availability requirements. The topic is addressed in depth in RAM Analysis: Reliability, Availability and Maintainability in Engineering.

Reliability Block Diagram — RBD

Reliability Block Diagrams — RBDs represent the functional logic required for a system to fulfill its mission. The diagram does not need to reproduce the physical arrangement of equipment; it should show which combinations of available and unavailable states preserve or interrupt the required function.

In a series structure, all blocks in the path must be available. If three independent elements have reliability of 0.99 during a mission, path reliability is approximately 0.99 × 0.99 × 0.99 = 0.9703. This illustrates why systems composed of many series elements can lose performance even when each individual component appears reliable.

In parallel, the logic changes: the function can remain available when at least one path continues operating. Two independent elements with reliability of 0.99 in simple parallel redundancy would yield a theoretical reliability of 1 − (0.01 × 0.01) = 0.9999. This calculation, however, is only valid when independence is defensible.

This is where many models overestimate performance. Two pieces of equipment may appear redundant and still share power supply, control, software, environment, maintenance procedures, or the same bus. A common-cause failure eliminates the benefit predicted by the parallel model. The RBD must therefore be combined with interface analysis, common failures, and degraded conditions.

In design practice, RBD is useful for comparing topologies, locating single points of failure, and technically justifying redundancy. It also helps answer whether the next investment should go to equipment duplication, infrastructure segregation, reduction of restoration time, or elimination of shared dependencies.

Fault Tree Analysis — FTA

Fault Tree Analysis — FTA begins with a clearly defined top event — for example, “total loss of supply to the critical load” — and logically decomposes which failures or combinations of events can produce it. AND gates represent situations in which events must occur together; OR gates represent alternative paths capable of causing the same result.

FTA becomes more useful when the top event is functional and verifiable. “Generator failure” is too narrow if the real concern is continuity of power; “unavailability of the emergency power-supply function” forces consideration of starting, fuel, protection, transfer, control, auxiliary power, and other dependencies.

When adequate data exist, the tree can be quantified to estimate the probability of the top event and identify minimal cut sets — minimum combinations of events capable of producing loss of function. Even when not quantified, the logical structure is already valuable for Design Review, risk analysis, incident investigation, and identification of single points of failure.

RBD and FTA examine the same system from complementary perspectives: RBD shows the paths required for functional success; FTA shows the paths that can lead to failure. In critical systems, using both perspectives helps avoid apparently redundant architectures that remain vulnerable to a single common dependency.

Weibull and life-data analysis

Statistical life models make it possible to explore how failure probability changes over time and whether observed behavior is compatible with early failures, approximately random events, or deterioration mechanisms. The Weibull distribution is widely used because its shape parameter can represent different regimes, but it should not be applied as an automatic curve to any history.

In practical terms, a shape parameter β below 1 is often compatible with a population in which the failure rate decreases over time, a scenario associated with early defects or implementation problems; β close to 1 is compatible with an approximately constant rate; β above 1 indicates an increasing rate, often associated with wear or deterioration. This interpretation needs to be confronted with the physical failure mechanism and with how the data were collected.

Another essential point is to consider censored data. Equipment that has not yet failed at the end of the observation period carries survival information and should not simply disappear from the sample. Ignoring censoring, mixing populations of different models or load regimes, and analyzing failures from different mechanisms as if they were a single population can severely distort the parameters.

Weibull is useful when the question requires understanding time behavior — for example, defining replacement policy, estimating life percentiles, comparing populations, or testing whether there is evidence of aging. When the decision only requires tracking aggregate operational frequency, other indicators may be sufficient. The technique must be proportional to the engineering question and data quality.

RCA and FRACAS

Root Cause Analysis — RCA — seeks to identify and eliminate mechanisms that produce recurring failures. The starting point must be evidence: sequence of events, operating condition, recent changes, alarm records, removed parts, measurements, history, and reproduction where possible. A cause described only as “human error,” “wear,” or “component failure” is usually still too superficial to guide an effective action.

Good RCA distinguishes physical cause, contributing factors, and organizational conditions. A damaged bearing may be the physical manifestation; misalignment, contamination, inadequate specification, poor installation, or incorrect lubrication may be mechanisms and underlying causes. Corrective action should act at the level capable of reducing recurrence, not merely replace the failed item.

A FRACAS — Failure Reporting, Analysis and Corrective Action System, in turn, transforms isolated occurrences into a closed-loop learning process. The minimum flow is to record the failure with consistent taxonomy, assess criticality, conduct a proportional analysis, define an action, assign responsibility and a deadline, verify implementation, and then confirm effectiveness. A failure should only be considered closed when the organization has evidence that the condition was treated or consciously accepted.

The value of FRACAS appears at portfolio level: recurrences that seemed independent begin to reveal patterns involving supplier, installation, environment, software, procedure, or design. This creates an improvement cycle in which operating data feed specifications, Design Reviews, maintenance plans, and new projects.

RCM

Reliability-Centered Maintenance — RCM uses functions, performance standards, functional failures, failure modes, and consequences to select technically justified maintenance policies. The central question is not “what preventive maintenance should be done on this equipment?” but “what needs to be done to preserve this function within its operating context?”.

This difference changes the logic of the plan. A failure mode with detectable degradation may justify condition-based maintenance. A mechanism clearly related to age may justify scheduled restoration or replacement. A protective function whose failure remains hidden may require periodic proof testing. In other cases, no preventive task is technically applicable and the correct response may be redesign or deliberate run-to-failure.

For a task to be accepted, it must be applicable to the failure mechanism and effective at controlling the consequence. Periodically replacing a component that fails randomly can increase cost and even introduce maintenance-induced failures without reducing risk. Likewise, installing sensors only creates value when the monitored variable has a demonstrated relationship with degradation and there is enough time to act before functional failure.

RCM is therefore a bridge between failure analysis and maintenance policy. The full methodology, including selection criteria and steps, is detailed in Reliability-Centered Maintenance (RCM).

Reliability begins in design

One of the most important differences between reliability engineering and traditional maintenance is when decisions are made. Many factors that will determine future availability are already defined when the asset enters operation.

During design, reliability engineering can establish:

  • quantitative and qualitative reliability and availability requirements;
  • redundancy and fault-tolerance criteria;
  • environmental conditions and mission profiles;
  • diagnostic and monitoring requirements;
  • accessibility and intervention-time requirements;
  • spare-parts philosophy;
  • isolation and replacement criteria;
  • testing and demonstration requirements;
  • documentation required for operation and maintenance.

IEC 60300-3-4 specifically addresses the specification of dependability requirements, including reliability, maintainability, supportability, and availability. This allows requirements to move from generic expectations to verifiable engineering criteria.

Relationship with asset management and lifecycle

Reliability engineering is a technical capability within a broader asset-management perspective. ISO 55000:2024 reinforces that asset management seeks to realize value from assets in alignment with organizational objectives, considering lifecycle, risk, performance, and resources.

Therefore, increasing reliability is not an absolute objective. The decision may involve balancing initial investment, redundancy, maintenance cost, energy consumption, spare-parts inventory, downtime risk, service life, and renewal capability.

This connection is especially important in decisions such as maintaining, refurbishing, recommissioning, modernizing, or replacing an asset. Engineering Asset Management structures this lifecycle perspective, while Reliability and Availability Engineering deepens the technical attributes that condition operational performance.

Relationship with maintenance engineering

Maintenance and reliability need to work from the same functional and data foundation. Maintenance engineering converts requirements and analyses into plans, routines, resources, backlog, inspections, spare parts, and indicators. Reliability, in turn, helps define where maintenance adds value and where the problem should be eliminated through design change, operating condition, or engineering.

A mature strategy avoids two extremes: over-maintaining low-criticality equipment and under-treating failure modes with relevant consequences.

The Maintenance Engineering service should therefore be connected to criticality, failure history, availability requirements, and degradation mechanisms, not merely to intervention calendars.

How to structure a reliability program

A reliability program needs scope and governance. A practical sequence can be organized into eight workstreams.

  1. Define relevant assets, systems, and boundaries.
  2. Establish functions, operating conditions, and performance requirements.
  3. Classify criticality considering technical and business consequences.
  4. Organize historical data, evidence, and information quality.
  5. Identify failure modes and degradation mechanisms.
  6. Select analysis methods compatible with the decision.
  7. Implement reliability-oriented design actions, maintenance, monitoring, or support.
  8. Measure results and continually review assumptions.

The program should not produce reports only. Each analysis must result in a traceable decision: specification change, architecture modification, new maintenance task, removal of a non-value-adding task, additional monitoring, critical spares, training, procedure review, or replacement plan.

Data required for a reliable analysis

Analysis quality depends on data quality. Maintenance databases without consistent taxonomy can mix functional failure, observed defect, planned intervention, and external outage, distorting indicators.

Useful data include technical register, asset hierarchy, operating hours, cycles, loading, environment, failure events, failure mode, cause, consequence, detection time, logistic time, repair time, parts used, costs, and post-intervention condition.

CMMS/EAM tools facilitate collection but do not replace data engineering and governance. The article on CMMS and integration with asset management explores this information layer in greater depth.

Example: redundancy does not guarantee availability

Consider a system with two pieces of equipment in redundancy, each theoretically capable of handling 100% of the load. The existence of the second unit may increase availability, but the result depends on other conditions.

If both share electrical supply, automation, cooling, communications, or maintenance procedures, common-cause failures may exist. If transfer to the standby unit depends on a slow manual action, the architecture may also fail to meet the required recovery time. And if periodic tests do not prove the availability of the redundancy, there is a risk of only apparent protection.

Reliability analysis therefore evaluates the functional system, its dependencies, and its failure modes — not merely the number of installed equipment items.

Reliability in existing systems

In brownfield assets, a reliable baseline often does not exist. Diagrams may be outdated, field changes may not have been incorporated into the As Built, maintenance histories may be incomplete, and actual conditions may differ from those originally anticipated.

In this scenario, the work may begin with survey, document validation, inspection, asset-hierarchy structuring, and condition characterization. Criticality and failure analyses can then produce a risk-based intervention roadmap.

When there is uncertainty about performance after modifications or long operating periods, Systems and Facilities Recommissioning can also provide evidence of functionality, integration, and performance.

When to engage reliability engineering

Reliability engineering tends to create the most value when the organization faces one or more of the following scenarios:

  • recurring shutdowns without an eliminated cause;
  • critical assets with insufficient history or low predictability;
  • expansion or modernization with high availability requirements;
  • difficulty prioritizing maintenance and investments;
  • excessive preventive maintenance without proportional reduction in failures;
  • need to justify redundancies or spare parts;
  • complex systems with dependencies and common-cause failures;
  • need to structure indicators and data quality;
  • transition from reactive maintenance to a risk- and condition-based approach;
  • decision between maintaining, refurbishing, recommissioning, or replacing assets.

The ultimate objective is not to produce the greatest possible number of analyses, but to build an objective basis for lifecycle decisions, balancing performance, cost, and risk.

A reliability program should end in verifiable decisions. Design changes, maintenance strategy, monitoring, spare parts, tests, and renewal must be traceable to the risk they are intended to control.

Learn about Reliability Engineering →

Technical references

[1] IEC. IEC 60300-1:2024 — Dependability management — Part 1: Managing dependability. Geneva: International Electrotechnical Commission, 2024.

[2] IEC. IEC 60300-3-4:2022 — Dependability management — Part 3-4: Application guide — Specification of dependability requirements. Geneva: International Electrotechnical Commission, 2022.

[3] IEC. IEC 60300-3-10:2025 — Dependability management — Part 3-10: Application guide — Maintainability and maintenance. Geneva: International Electrotechnical Commission, 2025.

[4] IEC. IEC 60812:2018 — Failure modes and effects analysis (FMEA and FMECA). Geneva: International Electrotechnical Commission, 2018.

[5] ISO. ISO 55000:2024 — Asset management — Vocabulary, overview and principles. Geneva: International Organization for Standardization, 2024.

Frequently asked questions
What is reliability engineering?

It is the engineering discipline that defines requirements and applies methods to understand failures, reliability, availability, and maintainability of systems and assets throughout the lifecycle.

What is the difference between reliability and availability?

Reliability relates to failure-free performance over a defined interval and under defined conditions. Availability also considers the ability to restore and keep the item ready for use when required.

Is reliability engineering the same as maintenance?

No. Maintenance is one interface. Reliability engineering also acts in design, specification, architecture, failure analysis, testing, data, and lifecycle decisions.

Which methods are used in reliability engineering?

Methods include FMEA/FMECA, RAM, RBD, FTA, Weibull analysis, RCA, FRACAS, RCM, and statistical analyses of failures and life data.

Is MTBF sufficient to measure reliability?

No. MTBF needs context and should normally be combined with failure distribution, criticality, degradation mechanisms, MTTR, availability, and other indicators.

When is a reliability analysis worthwhile?

When failures or downtime have relevant impact, when there are critical systems, recurring problems, redundancy decisions, modernization, investment prioritization, or a need to structure risk-based maintenance.

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