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Reliability engineering is a sub-discipline of systems engineering that emphasizes the ability of equipment to function without failure. Reliability describes the ability of a system or component to function under stated conditions for a specified period of time. Reliability is closely related to availability, which is typically described as the ability of a component or system to function at a specified moment or interval of time. The Reliability function is theoretically defined as the probability of success at time t, which is denoted R(t). This probability is estimated from detailed (physics of failure) analysis, previous data sets or through reliability testing and reliability modelling. Availability, Testability, maintainability and maintenance are often defined as a part of "reliability engineering" in reliability programs. Reliability often plays the key role in the cost-effectiveness of systems. Reliability engineering deals with the prediction, prevention and management of high levels of "lifetime" engineering uncertainty and risks of failure. Although stochastic parameters define and affect reliability, reliability is not only achieved by mathematics and statistics. "Nearly all teaching and literature on the subject emphasize these aspects, and ignore the reality that the ranges of uncertainty involved largely invalidate quantitative methods for prediction and measurement."O'Connor, Patrick D. T. (2002), ''Practical Reliability Engineering'' (Fourth Ed.), John Wiley & Sons, New York. . For example, it is easy to represent "probability of failure" as a symbol or value in an equation, but it is almost impossible to predict its true magnitude in practice, which is massively multivariate, so having the equation for reliability does not begin to equal having an accurate predictive measurement of reliability. Reliability engineering relates closely to Quality Engineering, safety engineering and system safety, in that they use common methods for their analysis and may require input from each other. It can be said that a system must be reliably safe. Reliability engineering focuses on costs of failure caused by system downtime, cost of spares, repair equipment, personnel, and cost of warranty claims.

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Overview

Objective

The objectives of reliability engineering, in decreasing order of priority, are: # To apply engineering knowledge and specialist techniques to prevent or to reduce the likelihood or frequency of failures. # To identify and correct the causes of failures that do occur despite the efforts to prevent them. # To determine ways of coping with failures that do occur, if their causes have not been corrected. # To apply methods for estimating the likely reliability of new designs, and for analysing reliability data. The reason for the priority emphasis is that it is by far the most effective way of working, in terms of minimizing costs and generating reliable products. The primary skills that are required, therefore, are the ability to understand and anticipate the possible causes of failures, and knowledge of how to prevent them. It is also necessary to have knowledge of the methods that can be used for analysing designs and data.

Scope and techniques

Reliability engineering for "complex systems" requires a different, more elaborate systems approach than for non-complex systems. Reliability engineering may in that case involve: * System availability and mission readiness analysis and related reliability and maintenance requirement allocation * Functional system failure analysis and derived requirements specification * Inherent (system) design reliability analysis and derived requirements specification for both hardware and software design * System diagnostics design * Fault tolerant systems (e.g. by redundancy) * Predictive and preventive maintenance (e.g. reliability-centered maintenance) * Human factors / human interaction / human errors * Manufacturing- and assembly-induced failures (effect on the detected "0-hour quality" and reliability) * Maintenance-induced failures * Transport-induced failures * Storage-induced failures * Use (load) studies, component stress analysis, and derived requirements specification * Software (systematic) failures * Failure / reliability testing (and derived requirements) * Field failure monitoring and corrective actions * Spare parts stocking (availability control) * Technical documentation, caution and warning analysis * Data and information acquisition/organisation (creation of a general reliability development hazard log and FRACAS system) * Chaos engineering Effective reliability engineering requires understanding of the basics of failure mechanisms for which experience, broad engineering skills and good knowledge from many different special fields of engineering are required, for example: * Tribology * Stress (mechanics) * Fracture mechanics / fatigue * Thermal engineering * Fluid mechanics / shock-loading engineering * Electrical engineering * Chemical engineering (e.g. corrosion) * Material science

Definitions

Reliability may be defined in the following ways: * The idea that an item is fit for a purpose with respect to time * The capacity of a designed, produced, or maintained item to perform as required over time * The capacity of a population of designed, produced or maintained items to perform as required over time * The resistance to failure of an item over time * The probability of an item to perform a required function under stated conditions for a specified period of time * The durability of an object

Basics of a reliability assessment

Many engineering techniques are used in reliability risk assessments, such as reliability block diagrams, hazard analysis, failure mode and effects analysis (FMEA), fault tree analysis (FTA), Reliability Centered Maintenance, (probabilistic) load and material stress and wear calculations, (probabilistic) fatigue and creep analysis, human error analysis, manufacturing defect analysis, reliability testing, etc. It is crucial that these analyses are done properly and with much attention to detail to be effective. Because of the large number of reliability techniques, their expense, and the varying degrees of reliability required for different situations, most projects develop a reliability program plan to specify the reliability tasks (statement of work (SoW) requirements) that will be performed for that specific system. Consistent with the creation of safety cases, for example per ARP4761, the goal of reliability assessments is to provide a robust set of qualitative and quantitative evidence that use of a component or system will not be associated with unacceptable risk. The basic steps to take are to: * Thoroughly identify relevant unreliability "hazards", e.g. potential conditions, events, human errors, failure modes, interactions, failure mechanisms and root causes, by specific analysis or tests. * Assess the associated system risk, by specific analysis or testing. * Propose mitigation, e.g. requirements, design changes, detection logic, maintenance, training, by which the risks may be lowered and controlled for at an acceptable level. * Determine the best mitigation and get agreement on final, acceptable risk levels, possibly based on cost/benefit analysis. Risk here is the combination of probability and severity of the failure incident (scenario) occurring. The severity can be looked at from a system safety or a system availability point of view. Reliability for safety can be thought of as a very different focus from reliability for system availability. Availability and safety can exist in dynamic tension as keeping a system too available can be unsafe. Forcing an engineering system into a safe state too quickly can force false alarms that impede the availability of the system. In a ''de minimis'' definition, severity of failures includes the cost of spare parts, man-hours, logistics, damage (secondary failures), and downtime of machines which may cause production loss. A more complete definition of failure also can mean injury, dismemberment, and death of people within the system (witness mine accidents, industrial accidents, space shuttle failures) and the same to innocent bystanders (witness the citizenry of cities like Bhopal, Love Canal, Chernobyl, or Sendai, and other victims of the 2011 Tōhoku earthquake and tsunami)—in this case, reliability engineering becomes system safety. What is acceptable is determined by the managing authority or customers or the affected communities. Residual risk is the risk that is left over after all reliability activities have finished, and includes the unidentified risk—and is therefore not completely quantifiable. The complexity of the technical systems such as improvements of design and materials, planned inspections, fool-proof design, and backup redundancy decreases risk and increases the cost. The risk can be decreased to ALARA (as low as reasonably achievable) or ALAPA (as low as practically achievable) levels.

Reliability and availability program plan

Reliability requirements

Reliability culture / human errors / human factors

In practice, most failures can be traced back to some type of human error, for example in: * Management decisions (e.g. in budgeting, timing, and required tasks) * Systems Engineering: Use studies (load cases) * Systems Engineering: Requirement analysis / setting * Systems Engineering: Configuration control * Assumptions * Calculations / simulations / FEM analysis * Design * Design drawings * Testing (e.g. incorrect load settings or failure measurement) * Statistical analysis * Manufacturing * Quality control * Maintenance * Maintenance manuals * Training * Classifying and ordering of information * Feedback of field information (e.g. incorrect or too vague) * etc. However, humans are also very good at detecting such failures, correcting for them, and improvising when abnormal situations occur. Therefore, policies that completely rule out human actions in design and production processes to improve reliability may not be effective. Some tasks are better performed by humans and some are better performed by machines. Furthermore, human errors in management; the organization of data and information; or the misuse or abuse of items, may also contribute to unreliability. This is the core reason why high levels of reliability for complex systems can only be achieved by following a robust systems engineering process with proper planning and execution of the validation and verification tasks. This also includes careful organization of data and information sharing and creating a "reliability culture", in the same way that having a "safety culture" is paramount in the development of safety critical systems.

Reliability prediction and improvement

Design for reliability

Design for Reliability (DfR) is a process that encompasses tools and procedures to ensure that a product meets its reliability requirements, under its use environment, for the duration of its lifetime. DfR is implemented in the design stage of a product to proactively improve product reliability. DfR is often used as part of an overall Design for Excellence (DfX) strategy.

Statistics-based approach (i.e. MTBF)

Reliability design begins with the development of a (system) model. Reliability and availability models use block diagrams and Fault Tree Analysis to provide a graphical means of evaluating the relationships between different parts of the system. These models may incorporate predictions based on failure rates taken from historical data. While the (input data) predictions are often not accurate in an absolute sense, they are valuable to assess relative differences in design alternatives. Maintainability parameters, for example Mean time to repair (MTTR), can also be used as inputs for such models. The most important fundamental initiating causes and failure mechanisms are to be identified and analyzed with engineering tools. A diverse set of practical guidance as to performance and reliability should be provided to designers so that they can generate low-stressed designs and products that protect, or are protected against, damage and excessive wear. Proper validation of input loads (requirements) may be needed, in addition to verification for reliability "performance" by testing. One of the most important design techniques is redundancy. This means that if one part of the system fails, there is an alternate success path, such as a backup system. The reason why this is the ultimate design choice is related to the fact that high-confidence reliability evidence for new parts or systems is often not available, or is extremely expensive to obtain. By combining redundancy, together with a high level of failure monitoring, and the avoidance of common cause failures; even a system with relatively poor single-channel (part) reliability, can be made highly reliable at a system level (up to mission critical reliability). No testing of reliability has to be required for this. In conjunction with redundancy, the use of dissimilar designs or manufacturing processes (e.g. via different suppliers of similar parts) for single independent channels, can provide less sensitivity to quality issues (e.g. early childhood failures at a single supplier), allowing very-high levels of reliability to be achieved at all moments of the development cycle (from early life to long-term). Redundancy can also be applied in systems engineering by double checking requirements, data, designs, calculations, software, and tests to overcome systematic failures. Another effective way to deal with reliability issues is to perform analysis that predicts degradation, enabling the prevention of unscheduled downtime events / failures. RCM (Reliability Centered Maintenance) programs can be used for this.

Physics-of-failure-based approach

For electronic assemblies, there has been an increasing shift towards a different approach called physics of failure. This technique relies on understanding the physical static and dynamic failure mechanisms. It accounts for variation in load, strength, and stress that lead to failure with a high level of detail, made possible with the use of modern finite element method (FEM) software programs that can handle complex geometries and mechanisms such as creep, stress relaxation, fatigue, and probabilistic design (Monte Carlo Methods/DOE). The material or component can be re-designed to reduce the probability of failure and to make it more robust against such variations. Another common design technique is component derating: i.e. selecting components whose specifications significantly exceed the expected stress levels, such as using heavier gauge electrical wire than might normally be specified for the expected electric current.

Common tools and techniques

Many of the tasks, techniques, and analyses used in Reliability Engineering are specific to particular industries and applications, but can commonly include: * Physics of failure (PoF) * Built-in self-test (BIT) (testability analysis) * Failure mode and effects analysis (FMEA) * Reliability hazard analysis * Reliability block-diagram analysis * Dynamic reliability block-diagram analysis * Fault tree analysis * Root cause analysis * Statistical engineering, design of experiments – e.g. on simulations / FEM models or with testing * Sneak circuit analysis * Accelerated testing * Reliability growth analysis (re-active reliability) * Weibull analysis (for testing or mainly "re-active" reliability) * Thermal analysis by finite element analysis (FEA) and / or measurement * Thermal induced, shock and vibration fatigue analysis by FEA and / or measurement * Electromagnetic analysis * Avoidance of single point of failure (SPOF) * Functional analysis and functional failure analysis (e.g., function FMEA, FHA or FFA) * Predictive and preventive maintenance: reliability centered maintenance (RCM) analysis * Testability analysis * Failure diagnostics analysis (normally also incorporated in FMEA) * Human error analysis * Operational hazard analysis * Preventative/Planned Maintenance Optimization (PMO) * Manual screening * Integrated logistics support Results from these methods are presented during reviews of part or system design, and logistics. Reliability is just one requirement among many for a complex part or system. Engineering trade-off studies are used to determine the optimum balance between reliability requirements and other constraints.

The importance of language

Reliability engineers, whether using quantitative or qualitative methods to describe a failure or hazard, rely on language to pinpoint the risks and enable issues to be solved. The language used must help create an orderly description of the function/item/system and its complex surrounding as it relates to the failure of these functions/items/systems. Systems engineering is very much about finding the correct words to describe the problem (and related risks), so that they can be readily solved via engineering solutions. Jack Ring said that a systems engineer's job is to "language the project." (Ring et al. 2000) For part/system failures, reliability engineers should concentrate more on the "why and how", rather that predicting "when". Understanding "why" a failure has occurred (e.g. due to over-stressed components or manufacturing issues) is far more likely to lead to improvement in the designs and processes used than quantifying "when" a failure is likely to occur (e.g. via determining MTBF). To do this, first the reliability hazards relating to the part/system need to be classified and ordered (based on some form of qualitative and quantitative logic if possible) to allow for more efficient assessment and eventual improvement. This is partly done in pure language and proposition logic, but also based on experience with similar items. This can for example be seen in descriptions of events in fault tree analysis, FMEA analysis, and hazard (tracking) logs. In this sense language and proper grammar (part of qualitative analysis) plays an important role in reliability engineering, just like it does in safety engineering or in-general within systems engineering. Correct use of language can also be key to identifying or reducing the risks of human error, which are often the root cause of many failures. This can include proper instructions in maintenance manuals, operation manuals, emergency procedures, and others to prevent systematic human errors that may result in system failures. These should be written by trained or experienced technical authors using so-called simplified English or Simplified Technical English, where words and structure are specifically chosen and created so as to reduce ambiguity or risk of confusion (e.g. an "replace the old part" could ambiguously refer to a swapping a worn-out part with a non-worn-out part, or replacing a part with one using a more recent and hopefully improved design).

Reliability modeling

Reliability modeling is the process of predicting or understanding the reliability of a component or system prior to its implementation. Two types of analysis that are often used to model a complete system's availability behavior including effects from logistics issues like spare part provisioning, transport and manpower are Fault Tree Analysis and Reliability Block Diagrams. At a component level, the same types of analyses can be used together with others. The input for the models can come from many sources including testing; prior operational experience; field data; as well as data handbooks from similar or related industries. Regardless of source, all model input data must be used with great caution, as predictions are only valid in cases where the same product was used in the same context. As such, predictions are often only used to help compare alternatives. For part level predictions, two separate fields of investigation are common: * The physics of failure approach uses an understanding of physical failure mechanisms involved, such as mechanical crack propagation or chemical corrosion degradation or failure; * The parts stress modelling approach is an empirical method for prediction based on counting the number and type of components of the system, and the stress they undergo during operation.

Reliability theory

Reliability is defined as the probability that a device will perform its intended function during a specified period of time under stated conditions. Mathematically, this may be expressed as, $R(t)=Pr\=\int_^ f(x)\, dx \ \!$, where $f\left(x\right) \!$ is the failure probability density function and $t$ is the length of the period of time (which is assumed to start from time zero). There are a few key elements of this definition: # Reliability is predicated on "intended function:" Generally, this is taken to mean operation without failure. However, even if no individual part of the system fails, but the system as a whole does not do what was intended, then it is still charged against the system reliability. The system requirements specification is the criterion against which reliability is measured. # Reliability applies to a specified period of time. In practical terms, this means that a system has a specified chance that it will operate without failure before time $t \!$. Reliability engineering ensures that components and materials will meet the requirements during the specified time. Note that units other than time may sometimes be used (e.g. "a mission", "operation cycles"). # Reliability is restricted to operation under stated (or explicitly defined) conditions. This constraint is necessary because it is impossible to design a system for unlimited conditions. A Mars Rover will have different specified conditions than a family car. The operating environment must be addressed during design and testing. That same rover may be required to operate in varying conditions requiring additional scrutiny. # Two notable references on reliability theory and its mathematical and statistical foundations are Barlow, R. E. and Proschan, F. (1982) and Samaniego, F. J. (2007).

Quantitative system reliability parameters—theory

Quantitative requirements are specified using reliability parameters. The most common reliability parameter is the mean time to failure (MTTF), which can also be specified as the failure rate (this is expressed as a frequency or conditional probability density function (PDF)) or the number of failures during a given period. These parameters may be useful for higher system levels and systems that are operated frequently (i.e. vehicles, machinery, and electronic equipment). Reliability increases as the MTTF increases. The MTTF is usually specified in hours, but can also be used with other units of measurement, such as miles or cycles. Using MTTF values on lower system levels can be very misleading, especially if they do not specify the associated Failures Modes and Mechanisms (The F in MTTF). In other cases, reliability is specified as the probability of mission success. For example, reliability of a scheduled aircraft flight can be specified as a dimensionless probability or a percentage, as often used in system safety engineering. A special case of mission success is the single-shot device or system. These are devices or systems that remain relatively dormant and only operate once. Examples include automobile airbags, thermal batteries and missiles. Single-shot reliability is specified as a probability of one-time success or is subsumed into a related parameter. Single-shot missile reliability may be specified as a requirement for the probability of a hit. For such systems, the probability of failure on demand (PFD) is the reliability measure – this is actually an "unavailability" number. The PFD is derived from failure rate (a frequency of occurrence) and mission time for non-repairable systems. For repairable systems, it is obtained from failure rate, mean-time-to-repair (MTTR), and test interval. This measure may not be unique for a given system as this measure depends on the kind of demand. In addition to system level requirements, reliability requirements may be specified for critical subsystems. In most cases, reliability parameters are specified with appropriate statistical confidence intervals.

Reliability testing

The purpose of reliability testing is to discover potential problems with the design as early as possible and, ultimately, provide confidence that the system meets its reliability requirements. Reliability testing may be performed at several levels and there are different types of testing. Complex systems may be tested at component, circuit board, unit, assembly, subsystem and system levels. (The test level nomenclature varies among applications.) For example, performing environmental stress screening tests at lower levels, such as piece parts or small assemblies, catches problems before they cause failures at higher levels. Testing proceeds during each level of integration through full-up system testing, developmental testing, and operational testing, thereby reducing program risk. However, testing does not mitigate unreliability risk. With each test both a statistical type 1 and type 2 error could be made and depends on sample size, test time, assumptions and the needed discrimination ratio. There is risk of incorrectly accepting a bad design (type 1 error) and the risk of incorrectly rejecting a good design (type 2 error). It is not always feasible to test all system requirements. Some systems are prohibitively expensive to test; some failure modes may take years to observe; some complex interactions result in a huge number of possible test cases; and some tests require the use of limited test ranges or other resources. In such cases, different approaches to testing can be used, such as (highly) accelerated life testing, design of experiments, and simulations. The desired level of statistical confidence also plays a role in reliability testing. Statistical confidence is increased by increasing either the test time or the number of items tested. Reliability test plans are designed to achieve the specified reliability at the specified confidence level with the minimum number of test units and test time. Different test plans result in different levels of risk to the producer and consumer. The desired reliability, statistical confidence, and risk levels for each side influence the ultimate test plan. The customer and developer should agree in advance on how reliability requirements will be tested. A key aspect of reliability testing is to define "failure". Although this may seem obvious, there are many situations where it is not clear whether a failure is really the fault of the system. Variations in test conditions, operator differences, weather and unexpected situations create differences between the customer and the system developer. One strategy to address this issue is to use a scoring conference process. A scoring conference includes representatives from the customer, the developer, the test organization, the reliability organization, and sometimes independent observers. The scoring conference process is defined in the statement of work. Each test case is considered by the group and "scored" as a success or failure. This scoring is the official result used by the reliability engineer. As part of the requirements phase, the reliability engineer develops a test strategy with the customer. The test strategy makes trade-offs between the needs of the reliability organization, which wants as much data as possible, and constraints such as cost, schedule and available resources. Test plans and procedures are developed for each reliability test, and results are documented. Reliability testing is common in the Photonics industry. Examples of reliability tests of lasers are life test and burn-in. These tests consist of the highly accelerated aging, under controlled conditions, of a group of lasers. The data collected from these life tests are used to predict laser life expectancy under the intended operating characteristics.

Reliability test requirements

Reliability test requirements can follow from any analysis for which the first estimate of failure probability, failure mode or effect needs to be justified. Evidence can be generated with some level of confidence by testing. With software-based systems, the probability is a mix of software and hardware-based failures. Testing reliability requirements is problematic for several reasons. A single test is in most cases insufficient to generate enough statistical data. Multiple tests or long-duration tests are usually very expensive. Some tests are simply impractical, and environmental conditions can be hard to predict over a systems life-cycle. Reliability engineering is used to design a realistic and affordable test program that provides empirical evidence that the system meets its reliability requirements. Statistical confidence levels are used to address some of these concerns. A certain parameter is expressed along with a corresponding confidence level: for example, an MTBF of 1000 hours at 90% confidence level. From this specification, the reliability engineer can, for example, design a test with explicit criteria for the number of hours and number of failures until the requirement is met or failed. Different sorts of tests are possible. The combination of required reliability level and required confidence level greatly affects the development cost and the risk to both the customer and producer. Care is needed to select the best combination of requirements—e.g. cost-effectiveness. Reliability testing may be performed at various levels, such as component, subsystem and system. Also, many factors must be addressed during testing and operation, such as extreme temperature and humidity, shock, vibration, or other environmental factors (like loss of signal, cooling or power; or other catastrophes such as fire, floods, excessive heat, physical or security violations or other myriad forms of damage or degradation). For systems that must last many years, accelerated life tests may be needed.

Accelerated testing

The purpose of accelerated life testing (ALT test) is to induce field failure in the laboratory at a much faster rate by providing a harsher, but nonetheless representative, environment. In such a test, the product is expected to fail in the lab just as it would have failed in the field—but in much less time. The main objective of an accelerated test is either of the following: * To discover failure modes * To predict the normal field life from the high stress lab life An Accelerated testing program can be broken down into the following steps: * Define objective and scope of the test * Collect required information about the product * Identify the stress(es) * Determine level of stress(es) * Conduct the accelerated test and analyze the collected data. Common ways to determine a life stress relationship are: * Arrhenius model * Eyring model * Inverse power law model * Temperature–humidity model * Temperature non-thermal model

Software reliability

Software reliability is a special aspect of reliability engineering. System reliability, by definition, includes all parts of the system, including hardware, software, supporting infrastructure (including critical external interfaces), operators and procedures. Traditionally, reliability engineering focuses on critical hardware parts of the system. Since the widespread use of digital integrated circuit technology, software has become an increasingly critical part of most electronics and, hence, nearly all present day systems. There are significant differences, however, in how software and hardware behave. Most hardware unreliability is the result of a component or material failure that results in the system not performing its intended function. Repairing or replacing the hardware component restores the system to its original operating state. However, software does not fail in the same sense that hardware fails. Instead, software unreliability is the result of unanticipated results of software operations. Even relatively small software programs can have astronomically large combinations of inputs and states that are infeasible to exhaustively test. Restoring software to its original state only works until the same combination of inputs and states results in the same unintended result. Software reliability engineering must take this into account. Despite this difference in the source of failure between software and hardware, several software reliability models based on statistics have been proposed to quantify what we experience with software: the longer software is run, the higher the probability that it will eventually be used in an untested manner and exhibit a latent defect that results in a failure (Shooman 1987), (Musa 2005), (Denney 2005). As with hardware, software reliability depends on good requirements, design and implementation. Software reliability engineering relies heavily on a disciplined software engineering process to anticipate and design against unintended consequences. There is more overlap between software quality engineering and software reliability engineering than between hardware quality and reliability. A good software development plan is a key aspect of the software reliability program. The software development plan describes the design and coding standards, peer reviews, unit tests, configuration management, software metrics and software models to be used during software development. A common reliability metric is the number of software faults, usually expressed as faults per thousand lines of code. This metric, along with software execution time, is key to most software reliability models and estimates. The theory is that the software reliability increases as the number of faults (or fault density) decreases. Establishing a direct connection between fault density and mean-time-between-failure is difficult, however, because of the way software faults are distributed in the code, their severity, and the probability of the combination of inputs necessary to encounter the fault. Nevertheless, fault density serves as a useful indicator for the reliability engineer. Other software metrics, such as complexity, are also used. This metric remains controversial, since changes in software development and verification practices can have dramatic impact on overall defect rates. Testing is even more important for software than hardware. Even the best software development process results in some software faults that are nearly undetectable until tested. As with hardware, software is tested at several levels, starting with individual units, through integration and full-up system testing. Unlike hardware, it is inadvisable to skip levels of software testing. During all phases of testing, software faults are discovered, corrected, and re-tested. Reliability estimates are updated based on the fault density and other metrics. At a system level, mean-time-between-failure data can be collected and used to estimate reliability. Unlike hardware, performing exactly the same test on exactly the same software configuration does not provide increased statistical confidence. Instead, software reliability uses different metrics, such as code coverage. Eventually, the software is integrated with the hardware in the top-level system, and software reliability is subsumed by system reliability. The Software Engineering Institute's capability maturity model is a common means of assessing the overall software development process for reliability and quality purposes.

Structural reliability

Structural reliability or the reliability of structures is the application of reliability theory to the behavior of structures. It is used in both the design and maintenance of different types of structures including concrete and steel structures. In structural reliability studies both loads and resistances are modeled as probabilistic variables. Using this approach the probability of failure of a structure is calculated.

Comparison to safety engineering

Reliability for safety and reliability for availability are often closely related. Lost availability of an engineering system can cost money. If a subway system is unavailable the subway operator will lose money for each hour the system is down. The subway operator will lose more money if safety is compromised. The definition of reliability is tied to a probability of not encountering a failure. A failure can cause loss of safety, loss of availability or both. It is undesirable to lose safety or availability in a critical system. Reliability engineering is concerned with overall minimisation of failures that could lead to financial losses for the responsible entity, whereas safety engineering focuses on minimising a specific set of failure types that in general could lead to loss of life, injury or damage to equipment. Reliability hazards could transform into incidents leading to a loss of revenue for the company or the customer, for example due to direct and indirect costs associated with: loss of production due to system unavailability; unexpected high or low demands for spares; repair costs; man-hours; re-designs or interruptions to normal production.Reliability and Safety Engineering – Verma, Ajit Kumar, Ajit, Srividya, Karanki, Durga Rao (2010) Safety engineering is often highly specific, relating only to certain tightly regulated industries, applications, or areas. It primarily focuses on system safety hazards that could lead to severe accidents including: loss of life; destruction of equipment; or environmental damage. As such, the related system functional reliability requirements are often extremely high. Although it deals with unwanted failures in the same sense as reliability engineering, it, however, has less of a focus on direct costs, and is not concerned with post-failure repair actions. Another difference is the level of impact of failures on society, leading to a tendency for strict control by governments or regulatory bodies (e.g. nuclear, aerospace, defense, rail and oil industries).

Fault tolerance

Safety can be increased using a 2oo2 cross checked redundant system. Availability can be increased by using "1oo2" (1 out of 2) redundancy at a part or system level. If both redundant elements disagree the more permissive element will maximize availability. A 1oo2 system should never be relied on for safety. Fault-tolerant systems often rely on additional redundancy (e.g. 2oo3 voting logic) where multiple redundant elements must agree on a potentially unsafe action before it is performed. This increases both availability and safety at a system level. This is common practice in Aerospace systems that need continued availability and do not have a fail-safe mode. For example, aircraft may use triple modular redundancy for flight computers and control surfaces (including occasionally different modes of operation e.g. electrical/mechanical/hydraulic) as these need to always be operational, due to the fact that there are no "safe" default positions for control surfaces such as rudders or ailerons when the aircraft is flying.

Basic reliability and mission reliability

The above example of a 2oo3 fault tolerant system increases both mission reliability as well as safety. However, the "basic" reliability of the system will in this case still be lower than a non-redundant (1oo1) or 2oo2 system. Basic reliability engineering covers all failures, including those that might not result in system failure, but do result in additional cost due to: maintenance repair actions; logistics; spare parts etc. For example, replacement or repair of 1 faulty channel in a 2oo3 voting system, (the system is still operating, although with one failed channel it has actually become a 2oo2 system) is contributing to basic unreliability but not mission unreliability. As an example, the failure of the tail-light of an aircraft will not prevent the plane from flying (and so is not considered a mission failure), but it does need to be remedied (with a related cost, and so does contribute to the basic unreliability levels).

Detectability and common cause failures

When using fault tolerant (redundant) systems or systems that are equipped with protection functions, detectability of failures and avoidance of common cause failures becomes paramount for safe functioning and/or mission reliability.

Reliability versus quality (Six Sigma)

Quality often focuses on manufacturing defects during the warranty phase. Reliability looks at the failure intensity over the whole life of a product or engineering system from commissioning to decommissioning. Six Sigma has its roots in statistical control in quality of manufacturing. Reliability engineering is a specialty part of systems engineering. The systems engineering process is a discovery process that is often unlike a manufacturing process. A manufacturing process is often focused on repetitive activities that achieve high quality outputs with minimum cost and time. The everyday usage term "quality of a product" is loosely taken to mean its inherent degree of excellence. In industry, a more precise definition of quality as "conformance to requirements or specifications at the start of use" is used. Assuming the final product specification adequately captures the original requirements and customer/system needs, the quality level can be measured as the fraction of product units shipped that meet specifications. Manufactured goods quality often focuses on the number of warranty claims during the warranty period. Quality is a snapshot at the start of life through the warranty period and is related to the control of lower-level product specifications. This includes time-zero defects i.e. where manufacturing mistakes escaped final Quality Control. In theory the quality level might be described by a single fraction of defective products. Reliability, as a part of systems engineering, acts as more of an ongoing assessment of failure rates over many years. Theoretically, all items will fail over an infinite period of time. Defects that appear over time are referred to as reliability fallout. To describe reliability fallout a probability model that describes the fraction fallout over time is needed. This is known as the life distribution model. Some of these reliability issues may be due to inherent design issues, which may exist even though the product conforms to specifications. Even items that are produced perfectly will fail over time due to one or more failure mechanisms (e.g. due to human error or mechanical, electrical, and chemical factors). These reliability issues can also be influenced by acceptable levels of variation during initial production. Quality and reliability are, therefore, related to manufacturing. Reliability is more targeted towards clients who are focused on failures throughout the whole life of the product such as the military, airlines or railroads. Items that do not conform to product specification will generally do worse in terms of reliability (having a lower MTTF), but this does not always have to be the case. The full mathematical quantification (in statistical models) of this combined relation is in general very difficult or even practically impossible. In cases where manufacturing variances can be effectively reduced, six sigma tools have been shown to be useful to find optimal process solutions which can increase quality and reliability. Six Sigma may also help to design products that are more robust to manufacturing induced failures and infant mortality defects in engineering systems and manufactured product. In contrast with Six Sigma, reliability engineering solutions are generally found by focusing on reliability testing and system design. Solutions are found in different ways, such as by simplifying a system to allow more of the mechanisms of failure involved to be understood; performing detailed calculations of material stress levels allowing suitable safety factors to be determined; finding possible abnormal system load conditions and using this to increase robustness of a design to manufacturing variance related failure mechanisms. Furthermore, reliability engineering uses system-level solutions, like designing redundant and fault-tolerant systems for situations with high availability needs (see Reliability engineering vs Safety engineering above). Note: A "defect" in six-sigma/quality literature is not the same as a "failure" (Field failure | e.g. fractured item) in reliability. A six-sigma/quality defect refers generally to non-conformance with a requirement (e.g. basic functionality or a key dimension). Items can, however, fail over time, even if these requirements are all fulfilled. Quality is generally not concerned with asking the crucial question "are the requirements actually correct?", whereas reliability is.

Reliability operational assessment

Once systems or parts are being produced, reliability engineering attempts to monitor, assess, and correct deficiencies. Monitoring includes electronic and visual surveillance of critical parameters identified during the fault tree analysis design stage. Data collection is highly dependent on the nature of the system. Most large organizations have quality control groups that collect failure data on vehicles, equipment and machinery. Consumer product failures are often tracked by the number of returns. For systems in dormant storage or on standby, it is necessary to establish a formal surveillance program to inspect and test random samples. Any changes to the system, such as field upgrades or recall repairs, require additional reliability testing to ensure the reliability of the modification. Since it is not possible to anticipate all the failure modes of a given system, especially ones with a human element, failures will occur. The reliability program also includes a systematic root cause analysis that identifies the causal relationships involved in the failure such that effective corrective actions may be implemented. When possible, system failures and corrective actions are reported to the reliability engineering organization. Some of the most common methods to apply to a reliability operational assessment are failure reporting, analysis, and corrective action systems (FRACAS). This systematic approach develops a reliability, safety, and logistics assessment based on failure/incident reporting, management, analysis, and corrective/preventive actions. Organizations today are adopting this method and utilizing commercial systems (such as Web-based FRACAS applications) that enable them to create a failure/incident data repository from which statistics can be derived to view accurate and genuine reliability, safety, and quality metrics. It is extremely important for an organization to adopt a common FRACAS system for all end items. Also, it should allow test results to be captured in a practical way. Failure to adopt one easy-to-use (in terms of ease of data-entry for field engineers and repair shop engineers) and easy-to-maintain integrated system is likely to result in a failure of the FRACAS program itself. Some of the common outputs from a FRACAS system include Field MTBF, MTTR, spares consumption, reliability growth, failure/incidents distribution by type, location, part no., serial no., and symptom. The use of past data to predict the reliability of new comparable systems/items can be misleading as reliability is a function of the context of use and can be affected by small changes in design/manufacturing.

Reliability organizations

Systems of any significant complexity are developed by organizations of people, such as a commercial company or a government agency. The reliability engineering organization must be consistent with the company's organizational structure. For small, non-critical systems, reliability engineering may be informal. As complexity grows, the need arises for a formal reliability function. Because reliability is important to the customer, the customer may even specify certain aspects of the reliability organization. There are several common types of reliability organizations. The project manager or chief engineer may employ one or more reliability engineers directly. In larger organizations, there is usually a product assurance or specialty engineering organization, which may include reliability, maintainability, quality, safety, human factors, logistics, etc. In such case, the reliability engineer reports to the product assurance manager or specialty engineering manager. In some cases, a company may wish to establish an independent reliability organization. This is desirable to ensure that the system reliability, which is often expensive and time-consuming, is not unduly slighted due to budget and schedule pressures. In such cases, the reliability engineer works for the project day-to-day, but is actually employed and paid by a separate organization within the company. Because reliability engineering is critical to early system design, it has become common for reliability engineers, however, the organization is structured, to work as part of an integrated product team.

Education

Some universities offer graduate degrees in reliability engineering. Other reliability professionals typically have a physics degree from a university or college program. Many engineering programs offer reliability courses, and some universities have entire reliability engineering programs. A reliability engineer must be registered as a professional engineer by the state or province by law, but not all reliability professionals are engineers. Reliability engineers are required in systems where public safety is at risk. There are many professional conferences and industry training programs available for reliability engineers. Several professional organizations exist for reliability engineers, including the American Society for Quality Reliability Division (ASQ-RD), the IEEE Reliability Society, the American Society for Quality (ASQ), and the Society of Reliability Engineers (SRE). A group of engineers have provided a list of useful tools for reliability engineering. These include: PTC Windchill software, RAM Commander software, RelCalc software, Military Handbook 217 (Mil-HDBK-217), 217Plus and the NAVMAT P-4855-1A manual. Analyzing failures and successes coupled with a quality standards process also provides systemized information to making informed engineering designs.

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References

*

* Barlow, R. E. and Proscan, F. (1981) Statistical Theory of Reliability and Life Testing, To Begin With Press, Silver Springs, MD. * Blanchard, Benjamin S. (1992), ''Logistics Engineering and Management'' (Fourth Ed.), Prentice-Hall, Inc., Englewood Cliffs, New Jersey. * Breitler, Alan L. and Sloan, C. (2005), Proceedings of the American Institute of Aeronautics and Astronautics (AIAA) Air Force T&E Days Conference, Nashville, TN, December, 2005: System Reliability Prediction: towards a General Approach Using a Neural Network. * Ebeling, Charles E., (1997), ''An Introduction to Reliability and Maintainability Engineering'', McGraw-Hill Companies, Inc., Boston. * Denney, Richard (2005) Succeeding with Use Cases: Working Smart to Deliver Quality. Addison-Wesley Professional Publishing. ISBN. Discusses the use of software reliability engineering in use case driven software development. * Gano, Dean L. (2007), "Apollo Root Cause Analysis" (Third Edition), Apollonian Publications, LLC., Richland, Washington * Holmes, Oliver Wendell Sr. The Deacon's Masterpiece * Kapur, K.C., and Lamberson, L.R., (1977), ''Reliability in Engineering Design'', John Wiley & Sons, New York. * Kececioglu, Dimitri, (1991) "Reliability Engineering Handbook", Prentice-Hall, Englewood Cliffs, New Jersey *Trevor Kletz (1998) ''Process Plants: A Handbook for Inherently Safer Design'' CRC * Leemis, Lawrence, (1995) ''Reliability: Probabilistic Models and Statistical Methods'', 1995, Prentice-Hall. * * MacDiarmid, Preston; Morris, Seymour; et al., (1995), ''Reliability Toolkit: Commercial Practices Edition'', Reliability Analysis Center and Rome Laboratory, Rome, New York. * Modarres, Mohammad; Kaminskiy, Mark; Krivtsov, Vasiliy (1999), "Reliability Engineering and Risk Analysis: A Practical Guide, CRC Press, . * Musa, John (2005) Software Reliability Engineering: More Reliable Software Faster and Cheaper, 2nd. Edition, AuthorHouse. ISBN * Neubeck, Ken (2004) "Practical Reliability Analysis", Prentice Hall, New Jersey * Neufelder, Ann Marie, (1993), ''Ensuring Software Reliability'', Marcel Dekker, Inc., New York. * O'Connor, Patrick D. T. (2002), ''Practical Reliability Engineering'' (Fourth Ed.), John Wiley & Sons, New York. . * Samaniego, Francisco J. (2007) "System Signatures and their Applications in Engineering Reliability", Springer (International Series in Operations Research and Management Science), New York. * Shooman, Martin, (1987), ''Software Engineering: Design, Reliability, and Management'', McGraw-Hill, New York. * Tobias, Trindade, (1995), ''Applied Reliability'', Chapman & Hall/CRC,
Springer Series in Reliability Engineering
* Nelson, Wayne B., (2004), ''Accelerated Testing—Statistical Models, Test Plans, and Data Analysis'', John Wiley & Sons, New York, * Bagdonavicius, V., Nikulin, M., (2002), "Accelerated Life Models. Modeling and Statistical analysis", CHAPMAN&HALL/CRC, Boca Raton, * Todinov, M. (2016), "Reliability and Risk Models: setting reliability requirements", Wiley, 978-1-118-87332-8.

US standards, specifications, and handbooks

Aerospace Report Number: TOR-2007(8583)-6889
''Reliability Program Requirements for Space Systems'', The Aerospace Corporation (10 July 2007)
DoD 3235.1-H (3rd Ed)
''Test and Evaluation of System Reliability, Availability, and Maintainability (A Primer)'', U.S. Department of Defense (March 1982).
NASA GSFC 431-REF-000370
''Flight Assurance Procedure: Performing a Failure Mode and Effects Analysis'', National Aeronautics and Space Administration Goddard Space Flight Center (10 August 1996).
IEEE 1332–1998
''IEEE Standard Reliability Program for the Development and Production of Electronic Systems and Equipment'', Institute of Electrical and Electronics Engineers (1998).
JPL D-5703
''Reliability Analysis Handbook'', National Aeronautics and Space Administration Jet Propulsion Laboratory (July 1990).
MIL-STD-785B
''Reliability Program for Systems and Equipment Development and Production'', U.S. Department of Defense (15 September 1980). (*Obsolete, superseded by ANSI/GEIA-STD-0009-2008 titled ''Reliability Program Standard for Systems Design, Development, and Manufacturing'', 13 Nov 2008)
MIL-HDBK-217F
''Reliability Prediction of Electronic Equipment'', U.S. Department of Defense (2 December 1991).
MIL-HDBK-217F (Notice 1)
''Reliability Prediction of Electronic Equipment'', U.S. Department of Defense (10 July 1992).
MIL-HDBK-217F (Notice 2)
''Reliability Prediction of Electronic Equipment'', U.S. Department of Defense (28 February 1995).
MIL-STD-690D
''Failure Rate Sampling Plans and Procedures'', U.S. Department of Defense (10 June 2005).
MIL-HDBK-338B
''Electronic Reliability Design Handbook'', U.S. Department of Defense (1 October 1998).
MIL-HDBK-2173
''Reliability-Centered Maintenance (RCM) Requirements for Naval Aircraft, Weapon Systems, and Support Equipment'', U.S. Department of Defense (30 January 1998); (superseded b
NAVAIR 00-25-403
.
MIL-STD-1543B
''Reliability Program Requirements for Space and Launch Vehicles'', U.S. Department of Defense (25 October 1988).
MIL-STD-1629A
''Procedures for Performing a Failure Mode Effects and Criticality Analysis'', U.S. Department of Defense (24 November 1980).
MIL-HDBK-781A
''Reliability Test Methods, Plans, and Environments for Engineering Development, Qualification, and Production'', U.S. Department of Defense (1 April 1996).
NSWC-06 (Part A & B)
''Handbook of Reliability Prediction Procedures for Mechanical Equipment'', Naval Surface Warfare Center (10 January 2006).
SR-332
''Reliability Prediction Procedure for Electronic Equipment'', Telcordia Technologies (January 2011).
FD-ARPP-01
''Automated Reliability Prediction Procedure'', Telcordia Technologies (January 2011).
GR-357
''Generic Requirements for Assuring the Reliability of Components Used in Telecommunications Equipment'', Telcordia Technologies (March 2001). http://standards.sae.org/ja1000/1_199903/ SAE JA1000/1 Reliability Program Standard Implementation Guide

UK standards

In the UK, there are more up to date standards maintained under the sponsorship of UK MOD as Defence Standards. The relevant Standards include: DEF STAN 00-40 Reliability and Maintainability (R&M) *PART 1: Issue 5: Management Responsibilities and Requirements for Programmes and Plans *PART 4: (ARMP-4)Issue 2: Guidance for Writing NATO R&M Requirements Documents *PART 6: Issue 1: IN-SERVICE R & M *PART 7 (ARMP-7) Issue 1: NATO R&M Terminology Applicable to ARMP's DEF STAN 00-42 RELIABILITY AND MAINTAINABILITY ASSURANCE GUIDES *PART 1: Issue 1: ONE-SHOT DEVICES/SYSTEMS *PART 2: Issue 1: SOFTWARE *PART 3: Issue 2: R&M CASE *PART 4: Issue 1: Testability *PART 5: Issue 1: IN-SERVICE RELIABILITY DEMONSTRATIONS DEF STAN 00-43 RELIABILITY AND MAINTAINABILITY ASSURANCE ACTIVITY *PART 2: Issue 1: IN-SERVICE MAINTAINABILITY DEMONSTRATIONS DEF STAN 00-44 RELIABILITY AND MAINTAINABILITY DATA COLLECTION AND CLASSIFICATION *PART 1: Issue 2: MAINTENANCE DATA & DEFECT REPORTING IN THE ROYAL NAVY, THE ARMY AND THE ROYAL AIR FORCE *PART 2: Issue 1: DATA CLASSIFICATION AND INCIDENT SENTENCING—GENERAL *PART 3: Issue 1: INCIDENT SENTENCING—SEA *PART 4: Issue 1: INCIDENT SENTENCING—LAND DEF STAN 00-45 Issue 1: RELIABILITY CENTERED MAINTENANCE DEF STAN 00-49 Issue 1: RELIABILITY AND MAINTAINABILITY MOD GUIDE TO TERMINOLOGY DEFINITIONS These can be obtained fro
DSTAN
There are also many commercial standards, produced by many organisations including the SAE, MSG, ARP, and IEE.

French standards

* FIDE

The FIDES methodology (UTE-C 80-811) is based on the physics of failures and supported by the analysis of test data, field returns and existing modelling. * UTE-C 80–810 or RDF200

The RDF2000 methodology is based on the French telecom experience.

International standards

TC 56 Standards: Dependability