# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 6 The UAV as a Complex Maintenance System
### The Failure Rate Paradox  
Now let's examine the real case of two completely different drones: a commercial drone and a surveillance drone. Both have on board, as payload, a system of stabilized cameras: in our reliability study, we will examine and compare only the systems and subsystems which they have in common. Let us now compare the reliability of the average commercial drone: a reliability profile has been created as a weighted average of the data present in our database, created through previous research. This is compared to an \"average\" military drone created according to official sources [64]. Furthermore, the reliability of all subsystems is compared; it is immediately evident that the distribution is different. According to Table 2, it is absolutely evident that the military drone, due to its complexity, has a reliability that is considerably inferior to that of a commercial drone which is certainly not built with stringent parameters and requirements. As is known, the commercial drone is composed entirely of COTS parts. Although there are not, for example, \"MIL\" reliability-level electronic components, the reliability of commercial electronic components is now extremely high, even those with plastic casing. This is also a consequence of the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Commercial Drone (a)**} \\ \hline
**System Description** & \(\lambda_{\text{P}}\) **System FIT (F/10\({}^{6}\) hrs)** & **MTBF (hours)** & **Incidence (\%)** \\ \hline Ground Control System & 2.00 & 500,000.0 & 6.62\% \\ Mainframe & 2.77 & 360,984.8 & 9.16\% \\ Power plant & 9.94 & 100,603.6 & 32.88\% \\ Navigation system & 9.41 & 106,269.9 & 31.13\% \\ Electronic system & 5.01 & 199,600.8 & 16.57\% \\ Payload & 1.10 & 909,090.9 & 3.64\% \\ \(\lambda\)**TOTAL =** & **30.23** & **FIT** & \\
**MTBF (R\({}_{\text{Total}}\)) =** & **33,079.50** & **Hours** & \\  & **1378.31** & **Days** & \\  & **49.23** & **Months** & \\ \hline \multicolumn{3}{c}{**Military Drone (b)**} \\ \hline
**System Description** & \(\lambda_{\text{P}}\) **System FIT (F/10\({}^{6}\) hrs)** & **MTBF (hours)** & **Incidence (\%)** \\ \hline Ground Control System & 14.00 & 71,403.6 & 27.30\% \\ Mainframe & 2.77 & 360,984.8 & 5.40\% \\ Power plant & 21.08 & 47,428.7 & 41.10\% \\ Navigation system & 7.39 & 135,369.3 & 14.40\% \\ Electronic system & 3.44 & 290,942.9 & 6.70\% \\ Payload & 2.62 & 382,219.2 & 5.10\% \\ \(\lambda\)**TOTAL =** & **51.30** & **FIT** & \\
**MTBF (R\({}_{\text{Total}}\)) =** & **19,493.18** & **Hours** & \\  & **812.22** & **Days** & \\  & **29.01** & **Months** & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the reliability of a commercial and a military drone. Figure 7: Uncertainty evaluation of corrective maintenance: original interval area (**red**) and the evaluation of the confidence interval (**green**). continuous research in the automotive field, where the operating temperature range is quite high. All of these aspects, combined with low construction complexity, lead to a high reliability level. The military drone, on the other hand, enjoys a high degree of redundancy and a knowledge of high quality components, but being an extremely sophisticated and complex product, it is heavily penalized from the point of view of the reliability figure. It must stated, however, that the capabilities of the latter compared to the commercial drone are noteworthy: greater range, greater autonomy, higher payload, and resistance to soft failure. These are all characteristics that are transparent to the calculation of reliability, and that then, eventually, lead to the paradox.