Template:Fleet analysis rsa

Fleet Analysis
Fleet analysis is similar to the repairable systems analysis described previously. The main difference is that a fleet of systems is considered and the models are 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 to enable the application of the Crow Extended model for fielded data. 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 Chapter 9.

Recall from Chapter 9 that in order to make projections using the Crow Extended model, the $$\beta $$  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 Repairable and Fleet illustrate this concept. Figure Repairable 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 Fleet illustrates this concept in the System Operation plot when you consider the Cum. Time Line. In this case, the $$\beta $$  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.