Availability

In reliability engineering, the term availability has the following meanings:

The degree to which a system, subsystem or equipment is in a specified operable and committable state at the start of a mission, when the mission is called for at an unknown, i.e. a random, time.
The probability that an item will operate satisfactorily at a given point in time when used under stated conditions in an ideal support environment.

Normally high availability systems might be specified as 99.98%, 99.999% or 99.9996%.

Introduction

Availability of a system is typically calculated as a function of its reliability, maintainability and its redundancy. As reliability increases and as redundancy increases, so does availability. As maintenance downtime decreases availability increases.

Availability of a system may also be increased by the strategy of focusing on minimizing downtime by increasing testability, diagnostics and maintainability. Improving maintainability during the early design phase is generally easier than reliability. Maintainability estimates (item repair [by replacement] rates) are also generally more accurate. However, because the uncertainties in the reliability estimates are in most cases very large, it is likely to dominate the availability (and the prediction uncertainty) problem, even while maintainability levels are very high. Furthermore, when reliability is not under control, then many and different sorts of issues may arise, for example:

Lack of trained and capable repair personnel
The need for detailed diagnostic procedures and other technical information
Spare part availability
Dead on arrival issues
Lack of repair facilities and tools

For high levels of system availability, such as the availability of engine thrust in an aircraft, the use of redundancy may be the best option to reduce the system failure rate. Refer to reliability engineering. Reliability needs to be evaluated and improved related to both availability and the cost of ownership due to cost of spare parts, maintenance man-hours, transport costs, storage cost, part obsolete risks etc. Often a trade-off is needed between the two. There might be a maximum ratio between availability and cost of ownership. The maintenance strategy can influence the reliability of a system (e.g. by preventive and/or predictive maintenance), although it can never bring it above the inherent reliability. So, Maintainability and Maintenance strategies influences the availability of a system. In theory this can be almost unlimited if one would be able to always repair any fault in an infinitely short time. This is in practice impossible. Repair-ability is always limited due to testability, manpower and logistic considerations. Reliability is not limited. Reliable items can be made that outlast the life of a machine with almost 100% certainty.

An availability plan should clearly provide a strategy for availability control. Whether only Availability or also Cost of Ownership is more important depends on the use of the system. For example, a system that is a critical link in a production system – e.g. a big oil platform – is normally allowed to have a very high cost of ownership if this translates to even a minor increase in availability, as the unavailability of the platform results in a massive loss of revenue which can easily exceed the high cost of ownership. A proper reliability plan should always address Reliability, Availability, Maintainability and Testability (RAMT) analysis in its total context.

Representation

The simplest representation of availability(A) is a ratio of the expected value of the uptime of a system to the aggregate of the expected values of up and down time, or

A={\frac {E[\mathrm {uptime} ]}{E[\mathrm {uptime} ]+E[\mathrm {downtime} ]}}

Another equation for availability(A) is a ratio of the Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR), or

A={\frac {MTBF}{MTBF+MTTR}}

If we define the status function $X(t)$ as

X(t)={\begin{cases}1,&{\text{sys functions at time }}t\\0,&{\text{maintenance}}\end{cases}}

therefore, the availability A(t) at time t > 0 is represented by

A(t)=\Pr[X(t)=1]=E[X(t)].\,

Average availability must be defined on an interval of the real line. If we consider an arbitrary constant $c>0$ , then average availability is represented as

A_{c}={\frac {1}{c}}\int _{0}^{c}A(t)\,dt.

Limiting (or steady-state) availability is represented by[1]

A=\lim _{c\rightarrow \infty }A_{c}.

Limiting average availability is also defined on an interval $[0,c]$ as,

A_{\infty }=\lim _{c\rightarrow \infty }A_{c}=\lim _{c\rightarrow \infty }{\frac {1}{c}}\int _{0}^{c}A(t)\,dt,\quad c>0.

Availability is the probability that an item will be in an operable and committable state at the start of a mission when the mission is called for at a random time, and is generally defined as uptime divided by total time (uptime plus downtime).

Methods and techniques to model availability

Reliability Block Diagrams or Fault Tree Analysis are developed to calculate availability of a system or a functional failure condition within a system including many factors like:

Reliability models
Maintainability models
Maintenance concepts
Redundancy
Common cause failure
Diagnostics
Level of repair
Repair status
Dormant failures
Test coverage
Active operational times / missions / sub system states
Logistical aspects like; spare part (stocking) levels at different depots, transport times, repair times at different repair lines, manpower availability and more.
Uncertainty in parameters

Furthermore, these methods are capable to identify the most critical items and failure modes or events that impact availability.

Definitions within systems engineering

Availability, inherent (A_i) [2] The probability that an item will operate satisfactorily at a given point in time when used under stated conditions in an ideal support environment. It excludes logistics time, waiting or administrative downtime, and preventive maintenance downtime. It includes corrective maintenance downtime. Inherent availability is generally derived from analysis of an engineering design:

The impact of a repairable-element (refurbishing/remanufacture isn't repair, but rather replacement) on the availability of the system, in which it operates, equals mean time between failures MTBF/(MTBF+ mean time to repair MTTR).
The impact of a one-off/non-repairable element (could be refurbished/remanufactured) on the availability of the system, in which it operates, equals the mean time to failure (MTTF)/(MTTF + the mean time to repair MTTR).

It is based on quantities under control of the designer.

Availability, achieved (Aa) [3] The probability that an item will operate satisfactorily at a given point in time when used under stated conditions in an ideal support environment (i.e., that personnel, tools, spares, etc. are instantaneously available). It excludes logistics time and waiting or administrative downtime. It includes active preventive and corrective maintenance downtime.

Availability, operational (Ao) [4] The probability that an item will operate satisfactorily at a given point in time when used in an actual or realistic operating and support environment. It includes logistics time, ready time, and waiting or administrative downtime, and both preventive and corrective maintenance downtime. This value is equal to the mean time between failure (MTBF) divided by the mean time between failure plus the mean downtime (MDT). This measure extends the definition of availability to elements controlled by the logisticians and mission planners such as quantity and proximity of spares, tools and manpower to the hardware item.

Refer to Systems engineering for more details

Basic example

If we are using equipment which has a mean time to failure (MTTF) of 81.5 years and mean time to repair (MTTR) of 1 hour:

MTTF in hours =

81.5 \times 365 \times 24 = 713940

(This is a reliability parameter and often has a high level of uncertainty!)

Inherent availability (Ai)

= 713940 / (713940+1) = 713940 / 713941 = 99.999860%

Inherent unavailability

= 1 / 713940 = 0.000140%

Outage due to equipment in hours per year = 1/rate = 1/MTTF = 0.01235 hours per year.

Literature

Availability is well established in the literature of stochastic modeling and optimal maintenance. Barlow and Proschan [1975] define availability of a repairable system as "the probability that the system is operating at a specified time t." Blanchard [1998] gives a qualitative definition of availability as "a measure of the degree of a system which is in the operable and committable state at the start of mission when the mission is called for at an unknown random point in time." This definition comes from the MIL-STD-721. Lie, Hwang, and Tillman [1977] developed a complete survey along with a systematic classification of availability.

Availability measures are classified by either the time interval of interest or the mechanisms for the system downtime. If the time interval of interest is the primary concern, we consider instantaneous, limiting, average, and limiting average availability. The aforementioned definitions are developed in Barlow and Proschan [1975], Lie, Hwang, and Tillman [1977], and Nachlas [1998]. The second primary classification for availability is contingent on the various mechanisms for downtime such as the inherent availability, achieved availability, and operational availability. (Blanchard [1998], Lie, Hwang, and Tillman [1977]). Mi [1998] gives some comparison results of availability considering inherent availability.

Availability considered in maintenance modeling can be found in Barlow and Proschan [1975] for replacement models, Fawzi and Hawkes [1991] for an R-out-of-N system with spares and repairs, Fawzi and Hawkes [1990] for a series system with replacement and repair, Iyer [1992] for imperfect repair models, Murdock [1995] for age replacement preventive maintenance models, Nachlas [1998, 1989] for preventive maintenance models, and Wang and Pham [1996] for imperfect maintenance models. A very comprehensive recent book is by Trivedi and Bobbio [2017].

Applications

Availability is used extensively in power plant engineering. For example, the North American Electric Reliability Corporation implemented the Generating Availability Data System in 1982.[5]

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References

Elsayed, E., Reliability Engineering, Addison Wesley, Reading, MA,1996
"Inherent Availability (AI)". Glossary of Defense Acquisition Acronyms and Terms. Department of Defense. Archived from the original on 13 April 2014. Retrieved 10 April 2014.
"Achieved Availability (AI)". Glossary of Defense Acquisition Acronyms and Terms. Department of Defense. Archived from the original on 13 April 2014. Retrieved 10 April 2014.
"Operational Availability (AI)". Glossary of Defense Acquisition Acronyms and Terms. Department of Defense. Archived from the original on 12 March 2013. Retrieved 10 April 2014.
"Mandatory Reporting of Conventional Generation Performance Data" (PDF). Generating Availability Data System. North American Electric Reliability Corporation. July 2011. pp. 7, 17. Retrieved 13 March 2014.

This article incorporates public domain material from the General Services Administration document: "Federal Standard 1037C". (in support of MIL-STD-188) K. Trivedi and A. Bobbio, Reliability and Availability Engineering: Modeling, Analysis and Applications, Cambridge University Press, 2017.

External links

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[1] Elsayed, E., Reliability Engineering, Addison Wesley, Reading, MA,1996

[2] "Inherent Availability (AI)". Glossary of Defense Acquisition Acronyms and Terms. Department of Defense. Archived from the original on 13 April 2014. Retrieved 10 April 2014.

[3] "Achieved Availability (AI)". Glossary of Defense Acquisition Acronyms and Terms. Department of Defense. Archived from the original on 13 April 2014. Retrieved 10 April 2014.

[4] "Operational Availability (AI)". Glossary of Defense Acquisition Acronyms and Terms. Department of Defense. Archived from the original on 12 March 2013. Retrieved 10 April 2014.

[5] "Mandatory Reporting of Conventional Generation Performance Data" (PDF). Generating Availability Data System. North American Electric Reliability Corporation. July 2011. pp. 7, 17. Retrieved 13 March 2014.