## 6 Experiments and Results
### Simulation of Autonomous Agricultural Vehicles (AAVs)
Two distributions are involved here: normal, with mean \(\mu\) and standard deviation \(\sigma\); and Poisson, with failure rate \(\lambda\) per 10,000 h.  
Analyzing the time scales of service processes and the occurrence of failures in KA-MAZ vehicles, the following relationship between model time (MTU) and real time was adopted: 1 MTU = 1 h.  
To obtain the integrated parameters of the car from the data of its components, we used the technique described in [54]. A mathematical model for determining the uptime probability was based on monitoring the vehicle mileage, the impact of factor influence, and the residual component life. As a result, for the considered example of modeling the fuel and braking systems, the integral stochastic parameters of failures were obtained. For example, Table 8 shows the time parameters of three AAVs given in the model time. At the initial time taken RT = 0 and TI = 0. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Failure Time Distribution** & **Values,** \\  & **h** \\ \hline Front axle modulator solenoid & Normal & \(\mu=11,675\); \(\sigma=587\) \\ Rear and center axle solenoid & Normal & \(\mu=10,900\); \(\sigma=1100\) \\ Brake drum & Normal & \(\mu=4140\); \(\sigma=295\) \\ Brake pad & Normal & \(\mu=2032\); \(\sigma=172\) \\ Pneumatic cylinder & Normal & \(\mu=11,080\); \(\sigma=1002\) \\ Air compressor bleed valve & Poisson & \(\lambda=0.4\) \\ Air compressor gear & Poisson & \(\lambda=0.4\) \\ Air compressor piston & Poisson & \(\lambda=0.38\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Brake system. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Failure Time Distribution** & **Value** \\ \hline Fuel nozzle & Poisson & \(\lambda=0.38\) \\ Fuel pump & Normal & \(\mu=6170\); \(\sigma=535\) \\ Fine fuel filter & Poisson & \(\lambda=0.32\) \\ Fuel strainer & Poisson & \(\lambda=0.46\) \\ Fuel pressure sensor & Normal & \(\mu=5583\); \(\sigma=513\) \\ Fuel temperature sensor & Normal & \(\mu=5760\); \(\sigma=540\) \\ Fuel pump drive & Normal & \(\mu=9200\); \(\sigma=750\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fuel system. Figure 9: TASK AV module with five phases of operations for autonomous agrotechnical vehicles. We evaluate the maintainability effectiveness by the schedule completion degree of the technological process. Factor \(Y\) is determined in the form of a task completion delay ratio obtained during statistical tests by the Monte Carlo method:  
\[Y=\frac{\mu(t_{A})-t_{Z}}{t_{Z}}\times 100\%,\]  
where \(t_{Z}\) is the task's scheduled completion time, \(t_{A}\) is the task's actual completion time, and \(\mu(t_{A})\) is the average actual completion time. For determining the required set of vehicles for the technological process, the AAV operating rate \(E_{j}\) is applied:  
\[E_{n}=\frac{\sum\limits_{k}t_{jk}}{\sum\limits_{k}t_{jk}}\times 100\%,k\in I _{j},\]  
where \(t_{jk}\) is the task \(z_{k}\) execution time, \(t_{jk}\) is the operation time for AAV with id = \(j\) during task \(z_{k}\) execution, and \(I_{j}\) is the index set of tasks to which \(j\)th car is assigned. The HPN model has been tested on multiple datasets corresponding to various repair and maintenance modes for autonomous vehicles. In the presented article, the results of the following experiment are given as an example: statistical tests were conducted under the normal distribution of the AAV maintenance time at the service station and a set of time values until preventive maintenance. Tables 15 and 16 contain the values of parameters of the hierarchical PN model, and show obtained factor \(Y\) and operating rate \(E\). In addition, the charts of factor \(Y\) and operating rate \(E\) are presented in Figures 10 and 11, illustrating the parameter interdependencies.