Downtime Distributions

Maintenance actions (preventive or corrective) are not instantaneous. There is a time associated with each action, i.e. the amount of time it takes to complete the action. This time is usually referred to as downtime and it is defined as the length of time an item is not operational. There are a number of different factors that can affect the length of downtime, such as the physical characteristics of the system, spare part availability, repair crew availability, human factors, environmental factors, etc. Downtime can be divided into two categories based on these factors:

Waiting Downtime. This is the time during which the equipment is inoperable but not yet undergoing repair. This could be due to the time it takes for replacement parts to be shipped, administrative processing time, etc.
Active Downtime. This is the time during which the equipment is inoperable and actually undergoing repair. In other words, the active downtime is the time it takes repair personnel to perform a repair or replacement. The length of the active downtime is greatly dependent on human factors, as well as on the design of the equipment. For example, the ease of accessibility of components in a system has a direct effect on the active downtime.

These downtime definitions are subjective and not necessarily mutually exclusive nor all-inclusive. As an example, consider the time required to diagnose the problem. One may need to diagnose the problem before ordering parts and then wait for the parts to arrive.

The influence of a variety of different factors on downtime results in the fact that the time it takes to repair/restore a specific item is not generally constant. That is, the time-to-repair is a random variable, much like the time-to-failure. The statement that "it takes on average five hours to repair" implies an underlying probabilistic distribution. Distributions that describe the time-to-repair are called repair distributions (or downtime distributions) in order to distinguish them from the failure distributions. However, the methods employed to quantify these distributions are not any different mathematically from the methods employed to quantify failure distributions. The difference is in how they are employed (i.e. the events they describe and metrics used). As an example, when using a life distribution with failure data (i.e. the event modeled was time-to-failure), unreliability provides the probability that the event (failure) will occur by that time, while reliability provides the probability that the event (failure) will not occur. In the case of downtime distributions, the data set consists of times-to-repair, thus what we termed as unreliability now becomes the probability of the event occurring (i.e. repairing the component). Using these definitions, the probability of repairing the component by a given time, t, is also called the component's maintainability.

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