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Reliability HotWire |
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Reliability Basics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

When analyzing a systems reliability and
availability, measuring the importance of components is often of significant
value in prioritizing improvement efforts, performing trade-off analysis in
system design or suggesting the most efficient way to operate and maintain a
system. Focusing on the most problematic areas in the system results in the
most significant gains. This article presents different ways for assessing
the importance of non-repairable and repairable components within a system
using
With modern technology and higher reliability requirements, systems are getting more complicated. Therefore, identifying the most problematic components can become difficult. Many systems are repairable systems composed of many components that fail and get repaired based on different distributions. With limitations and constraints (such as spare parts availability, repair crew response time, logistic delays etc.), exact analytical solutions become intractable. In these cases, simulation becomes the tool of choice in modeling repairable systems and identifying weak components and areas where maintainability limitations hinder the availability of the system.
Using Reliability Importance ( I, of component _{Ri}i in
a system of n components is given by Leemis [2]:
Where: -
*R*is the system reliability at a certain time,_{s}(t)*t* -
*R*is the component reliability at a certain time,_{i}(t)*t*
This metric measures the rate of change (at time
As an example, let us consider the system described in Figure 1.
The failure distributions for the components in the diagram are:
The system reliability equation for this configuration can be expressed as:
Hence, according to Eqn. (1), the reliability importance of component A, for example, is:
By varying the time value,
This type of reliability importance measure can
be presented graphically in various ways. The following
The next plot is a snapshot of the previous plot at a specific time value (this is called "static reliability importance").
The following plot is also static reliability importance, but is presented as a "square pie chart" that shows the breakdown of the components reliability importance.
The three plots above show the clear dominance of two (20%) of the components, A and I, in responsibility for most of the failures of the system.
Through simulation, the system and components
histories over time can be captured. The results of the simulation can be
used to quantify two other types of reliability importance measures,
This metric considers only failure events and excludes preventive maintenance and inspection events that cause an interruption is the systems operation.
RS FCI reports the percentage of times that a
system failure event was caused (triggered) by a failure of a particular
component over the simulation time (0, For example, if we simulate the systems operation for 5000 hr in BlockSim, we obtain the following Block Summary report.
For component A, RS FCI = 73.73%. This implies that 73.73% of the times that the system failed, a component A failure was responsible. Note that the RS FCI of A and I is 81.67%. In other words, A and I contributed to about 80% of the systems total downing failures. The RS FCI results can also be seen in a graphical format.
This metric considers all downing events,
In Figure 2, we see that for component A, RS
DECI = 51.68%. This implies that 51.68% of the times that the system was
down were due to component A being down. Note that the RS DECI of A and I is
80.05%. Once again we see how the The RS DECI results can also be seen in a graphical format.
For the repairable system example in Figure 1, the FRED report is shown next. The FRED report shows the average availability, the MTBF, the MTTR (mean time to repair) and the RS FCI values for each component in the system. In addition, the components are color coded (a color spectrum varying from red, for worst reliability, to dark green for best reliability) to show the reliability of each component in relation to the other components. For example, we can conclude from the above FRED report that component Gs reliability needs improvement and that component Bs maintainability needs improvement (MTTR=678.71h).
In
The above analysis can be used to weigh the
gains obtained by switching to a more expensive supplier. The next example shows the impact on availability if each preventive maintenance policy applied on the components is performed every 200 hr of component age.
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