Standards are produced by both governmental agencies and professional associations, and international standards bodies such as. The following table lists selected standards from each of these agencies. Because of differences in domains and because many standards handle the same topic in slightly different ways, selection of the appropriate requires consideration of previous practices often documented as contractual requirements , domain specific considerations, certification agency requirements, end user requirements if different from the acquisition or producing organization , and product or system characteristics.
Becoming a reliability engineer requires education in probability and statistics as well as the specific engineering domain of the product or system under development or in operation. A number of universities throughout the world have departments of reliability engineering which also address maintainability and availability and more have research groups and courses in reliability and safety — often within the context of another discipline such as computer science, system engineering, civil engineering, mechanical engineering, or bioengineering.
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Because most academic engineering programs do not have a full reliability department, most engineers working in reliability have been educated in other disciplines and acquire the additional skills through additional coursework or by working with other qualified engineers. However, only a minority of engineers working in the discipline have this certification. Reliability can be characterized in terms of the parameters, mean, or any percentile of a reliability distribution.
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However, in most cases, the exponential distribution is used, and a single value, the mean time to failure MTTF for non-restorable systems, or mean time between failures MTBF for restorable systems are used. The metric is defined as. Maintainability is often characterized in terms of the exponential distribution and the mean time to repair and be similarly calculated, i. Where is the total down time and noutages is the number of outages. As was noted above, accounting for downtime requires definitions and specificity.
Down time might be counted only for corrective maintenance actions, or it may include both corrective and preventive maintenance actions.
Where the lognormal rather than the exponential distribution is used, a mean down time can still be calculated, but both the log of the downtimes and the variance must be known in order to fully characterize maintainability. As was the case with maintainability, availability may be qualified as to whether it includes only unplanned failures and repairs inherent availability or downtime due to all causes including administrative delays, staffing outages, or spares inventory deficiencies operational availability.
Probabilistic metrics describe system performance for RAM. Quantiles, means, and modes of the distributions used to model RAM are also useful. Availability has some additional definitions, characterizing what downtime is counted against a system. For inherent availability , only downtime associated with corrective maintenance counts against the system. For achieved availability , downtime associated with both corrective and preventive maintenance counts against a system.
Finally, operational availability counts all sources of downtime, including logistical and administrative, against a system. Availability can also be calculated instantaneously, averaged over an interval, or reported as an asymptotic value. Asymptotic availability can be calculated easily, but care must be taken to analyze whether or not a systems settles down or settles up to the asymptotic value, as well as how long it takes until the system approaches that asymptotic value.
It is defined as the partial derivative of the system reliability with respect to the reliability of a component. Criticality is a guide to prioritizing reliability improvement efforts. Many of these metrics cannot be calculated directly because the integrals involved are intractable. They are usually estimated using simulation.
There are a wide range of models that estimate and predict reliability Meeker and Escobar System models are used to 1 combine probabilities or their surrogates, failure rates and restoration times, at the component level to find a system level probability or 2 to evaluate a system for maintainability, single points of failure, and failure propagation. The three most common are reliability block diagrams, fault trees, and failure modes and effects analyses.
There are more sophisticated probability models used for life data analysis. These are best characterized by their failure rate behavior, which is defined as the probability that a unit fails in the next small interval of time, given it has lived until the beginning of the interval, and divided by the length of the interval. Models can be considered for a fixed environmental condition.
They can also be extended to include the effect of environmental conditions on system life. Such extended models can in turn be used for accelerated life testing ALT , where a system is deliberately and carefully overstressed to induce failures more quickly. The data is then extrapolated to usual use conditions.
This is often the only way to obtain estimates of the life of highly reliable products in a reasonable amount of time Nelson Also useful are degradation models , where some characteristic of the system is associated with the propensity of the unit to fail Nelson As that characteristic degrades, we can estimate times of failure before they occur.
The initial developmental units of a system often do not meet their RAM specifications. Reliability growth models allow estimation of resources particularly testing time necessary before a system will mature to meet those goals Meeker and Escobar Maintainability models describe the time necessary to return a failed repairable system to service. They are usually the sum of a set of models describing different aspects of the maintenance process e. These models often have threshold parameters, which are minimum times until an event can occur.
Logistical support models attempt to describe flows through a logistics system and quantify the interaction between maintenance activities and the resources available to support those activities.
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Queue delays, in particular, are a major source of down time for a repairable system. A logistical support model allows one to explore the trade space between resources and availability. All these models are abstractions of reality, and so at best approximations to reality. To the extent they provide useful insights, they are still very valuable.
The more complicated the model, the more data necessary to estimate it precisely. The greater the extrapolation required for a prediction, the greater the imprecision. Extrapolation is often unavoidable, because high reliability equipment typically can have long life and the amount of time required to observe failures may exceed test times. This requires strong assumptions be made about future life such as the absence of masked failure modes and that these assumptions increase uncertainty about predictions.
The uncertainty introduced by strong model assumptions is often not quantified and presents an unavoidable risk to the system engineer. There are many ways to characterize the reliability of a system, including fault trees, reliability block diagrams, and failure mode effects analysis. A Fault Tree Kececioglu is a graphical representation of the failure modes of a system. Fault trees can be complete or partial; a partial fault tree focuses on a failure mode or modes of interest.
They allow 'drill down' to see the dependencies of systems on nested systems and system elements. Fault trees were pioneered by Bell Labs in the s. A Failure Mode Effects Analysis is a table that lists the possible failure modes for a system, their likelihood, and the effects of the failure. A Failure Modes Effects Criticality Analysis scores the effects by the magnitude of the product of the consequence and likelihood, allowing ranking of the severity of failure modes Kececioglu A Reliability Block Diagram RBD is a graphical representation of the reliability dependence of a system on its components.
It is a directed, acyclic graph.
Reliability of Computer Systems and Networks
Each path through the graph represents a subset of system components. As long as the components in that path are operational, the system is operational. Component lives are usually assumed to be independent in a RBD. Simple topologies include a series system, a parallel system, a k of n system, and combinations of these. These hierarchical models allow the analyst to have the appropriate resolution of detail while still permitting abstraction.
System models require even more data to fit them well.
The specialized analyses required for RAM drive the need for specialized software. While general purpose statistical languages or spreadsheets can, with sufficient effort, be used for reliability analysis, almost every serious practitioner uses specialized software.
Minitab versions 13 and later includes functions for life data analysis. Win Smith is a specialized package that fits reliability models to life data and can be extended for reliability growth analysis and other analyses. Relex has an extensive historical database of component reliability data and is useful for estimating system reliability in the design phase.
There is also a suite of products from ReliaSoft that is useful in specialized analyses. ALTA fits accelerated life models to accelerated life test data. BlockSim models system reliability, given component data. In addition to these comprehensive tool families, there are more narrowly scoped tools. Some general purpose statistical analysis software include functions for reliability data analysis. Glossary: Reliability. Accessed on September 11, Department of Defense DoD.
Ebeling, Charles E. A: Waveland Press. IEEE Std Kececioglu, D. Reliability Engineering Handbook, Volume 2. Laprie, J. Avizienis, and B.