## 7 The Experimental Results and Discussion
### The Maintainability Index Computation for State-of-the-Art Solar Tracking Systems
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Research Title** & \(\mathbf{T_{NC}}\) & \(\mathbf{N_{MC}}\) & \(\mathbf{MF}\) & \(\mathbf{N_{AC}}\) & \(\mathbf{AS}\) & \(\mathbf{N_{TD}}\) & \(\mathbf{PME}\) & \(\mathbf{MI}\) \\ \hline \({}^{a}\)Performance Enhancement of Solar Panel Using & 15 & 10 & 66.6\% & 9 & 60\% & 0 & 0\% & 37.98\% \\ Dual Axis Solar Tracker\({}^{a}\)[27] & 10 & 10 & 100\% & 6 & 60\% & 0 & 0\% & 48\% \\ \hline \({}^{a}\)Design and Implementation of a Solar Tracker & 14 & 10 & 71.42\% & 6 & 100\% & 0 & 0\% & 51.42\% \\ System with Dual Axis for Photovoltaic Panels in El Oued Region of Algeria\({}^{a}\)[74] & 10 & 10 & 100\% & 8 & 80\% & 1 & 10\% & 58\% \\ \hline \({}^{a}\)Dual-axis solar tracker based on predictive control algorithms\({}^{a}\)[75] & 38 & 32 & 84.21\% & 35 & 92.1\% & 8 & 21.05\% & 61.28\% \\ \hline \({}^{a}\)Digital Twin Architecture for an Automated PV System with Self-Testing Capabilities\({}^{a}\)[82] & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Maintainability index computation across several dual-axis solar tracking system designs. design that can withstand the severe weather conditions in desert climate zones [74], far from rural and urban areas. One way to assess dependability is by computing the MI score for several solar tracking architectures, as shown in Table 3. While general considerations declare that a lower number of available components (T\({}_{\text{NC}}\)) usually results in higher maintainability, comparing the works in [27; 28], we can observe that modularity improves the MI result of the solar tracker design in [28] by 10% compared to the work in [27]. Similarly, modularity significantly benefits the solar tracking architecture described in [63], resulting in a higher MI score than the system depicted in [74]. Finally, considering the scenario where two solar tracking systems make use of predictive control strategies for ensuring increased energy production [75; 82], the number of employed test devices slightly improves the maintainability of the installation. Conclusively, solar tracking systems with a lower T\({}_{\text{NC}}\) value are easier to maintain, whereas devices equipped with a greater number of electronic devices can compensate by integrating sensor modules and test devices to improve their dependability. Besides its benchmarking capabilities, the maintenance-oriented metric system can be used to reduce the MTTR parameter of solar tracking systems. To determine the T\({}_{\text{HR}}\) parameter, it is mandatory to formulate a maintenance scenario for a one year schedule. For example, let us assume that a repair team has spent 50 h on unplanned maintenance for a solar tracking system that has broken down eight times over a year. According to Equation (21), the MTTR is computed as in Equation (42):  
\[MTTR=\frac{T_{MT}}{T_{NF}}=\frac{50}{8}=6.25\text{ h} \tag{42}\]  
What constitutes a superior \(MTTR\) depends on several parameters, including asset type, criticality, and age. However, an appropriate guideline is an \(MTTR\) of less than 5 h. Therefore, by following the mathematical rules presented in Equations (22) to (26), we determine the time hour reduction functions correlated with each of the three formulated metrics. Regarding modularity, if \(MF=100\%\) equals a 2 h reduction:  
\[f_{MF}(MF)=k_{1}\times\frac{MF}{100}=1.68\times\frac{84.21}{100}=1.68\times 0.84=1.41\text{ h}, \tag{43}\]  
where \(k_{1}=1.68\) represents the hour reduction per modularity percentage, and \(MF=84.21\%\) is the computed modularity value from Equation (38). Thus, the time hour reduction using the modularity metric is 1.41 h. Regarding accessibility, by considering that for \(AS=100\%\) we have a 1.5 h reduction in repair time:  
\[f_{AS}(AS)=k_{2}\times\frac{AS}{100}=1.38\times\frac{92.10}{100}=1.38\times 0.92=1.27\text{ h}, \tag{44}\]  
where \(k_{2}=1.38\) represents the hour reduction per accessibility percentage, and \(AS=92.10\%\) is the computed accessibility value from Equation (39).