Fleet analysis is similar to the repairable
systems analysis described in the previous section. The main difference
is that a fleet of systems is considered and the Power Law model is applied
to the fleet failures rather than to the system failures. In other words,
repairable system analysis models the number of system failures versus
system time; whereas fleet analysis models the number of fleet failures
versus fleet time.
The main motivation for fleet analysis is the application of the Crow
Extended model. In many cases, reliability improvements might be necessary
on systems that are already in the field. These types of reliability improvements
are essentially delayed fixes (BD modes) as described in the Crow
Extended chapter of this on-line reference.
Recall from the Crow Extended chapter that in order to make projections
using the Crow Extended model, the of the
combined A and BD modes should be equal to 1. Since the failure intensity
in a fielded system might be changing over time (e.g.
increasing if the system wears out), this assumption might be violated.
In such a scenario, the Crow Extended model cannot be used. However, if
a fleet of systems is considered and the number of fleet failures versus
fleet time is modeled, the failures might become random. This is because
there is a mixture of systems within a fleet, new and old, and when the
failures of this mixture of systems are viewed from a cumulative fleet
time point of view, they may be random. Figures 10.5 and 10.6 illustrate
this concept. Figure 10.5 shows the number of failures over system age.
It can be clearly seen that as the systems age, the intensity of the failures
increases (wearout). The superposition system line, which brings the failures
from the different systems under a single timeline, also illustrates this
observation. On the other hand, if you take the same four systems and
combine their failures from a fleet perspective, and consider fleet failures
over cumulative fleet hours, then the failures seem to be random. Figure
10.6 illustrates this concept in the System Operation plot when you consider
the Cum. Time Line.
Figure 10.5: Repairable
Systems Operation plot
Figure 10.6: Fleet System
Operation plot
In this case, the of the fleet will
be equal to 1 and the Crow Extended model can be used for quantifying
the effects of future reliability improvements on the fleet.
This section of the on-line reference includes the following
subsections:
of the
combined A and BD modes should be equal to 1. Since the failure intensity
in a fielded system might be changing over time (e.g.
increasing if the system wears out), this assumption might be violated.
In such a scenario, the Crow Extended model cannot be used. However, if
a fleet of systems is considered and the number of fleet failures versus
fleet time is modeled, the failures might become random. This is because
there is a mixture of systems within a fleet, new and old, and when the
failures of this mixture of systems are viewed from a cumulative fleet
time point of view, they may be random. Figures 10.5 and 10.6 illustrate
this concept. Figure 10.5 shows the number of failures over system age.
It can be clearly seen that as the systems age, the intensity of the failures
increases (wearout). The superposition system line, which brings the failures
from the different systems under a single timeline, also illustrates this
observation. On the other hand, if you take the same four systems and
combine their failures from a fleet perspective, and consider fleet failures
over cumulative fleet hours, then the failures seem to be random. Figure
10.6 illustrates this concept in the System Operation plot when you consider
the Cum. Time Line.
of the fleet will
be equal to 1 and the Crow Extended model can be used for quantifying
the effects of future reliability improvements on the fleet.