## 7 The Experimental Results and Discussion
### The Maintainability Index Computation for State-of-the-Art Solar Tracking Systems  
The experimental results regarding maintainability can be obtained by applying the maintenance-oriented metric system to the proposed solar tracking prototype. The determined maintainability value is then compared to the score distribution of other dual-axis solar trackers from related research. To estimate the MI, several other parameters must be computed first according to the steps presented in Section 6.2. The modularity of the solar tracking device is given by the number of components which, in case of malfunction, can be easily swapped during maintenance operations. According to the solar tracking prototype's hardware configuration, the MF can be computed by applying the Equation (15) as follows:  
\[MF=\frac{N_{\text{MC}}}{T_{\text{NC}}}\times 100=\frac{32}{38}\times 100=84.21 \%, \tag{38}\]  
where \(N_{\text{MC}}\) = 32 represents the number of easily replaceable electronic components, and \(T_{\text{MC}}\) = 38 represents the total number of electronic components available in the system. Figure 13: Probability–failure curve simulation based on reconfigurability–reliability dependency. Therefore, the entire solar tracking device has a modularity score of 84.21%. Next, the \(AS\) can be calculated by using Equation (16), as follows:  
\[AS=\frac{N_{AC}}{T_{NC}}\times 100=\frac{35}{38}\times 100=92.10\%, \tag{39}\]  
where \(N_{AC}\) = 35 designates the number of easily reachable electronic components, and \(T_{MC}\) = 38 represents the total number of electronic components available in the system. Therefore, the entire solar tracking device has an accessibility value of 92.10%. Following, the _PME_ is determined via Equation (17) as follows:  
\[PME=\frac{N_{TD}}{T_{NC}}\times 100=\frac{8}{38}\times 100=21.05\%, \tag{40}\]  
where \(N_{TD}\) = 8 refers to the total number of system health monitoring devices (1 \(\times\) temperature sensor, 2 \(\times\) rotary encoders, 2 \(\times\) LFSR units, 1 \(\times\) reconfigurable MISR, and 1 \(\times\) comparator), while \(T_{MC}\) = 38 denotes the total number of electronic components available in the system, regardless of their testing features. Thus, the entire solar tracking system has a predictive maintenance score of 21.05% considering all testing devices. The MI computation, on the other hand, depends on the initial value selection of the weights associated with the previously determined metrics. Since the priority of this work is set toward integrating effective testing facilities for solar tracking systems, let us assume the following weight factor distribution: \(w_{1}\) = 0.3 (30% importance for modularity); \(w_{2}\) = 0.3 (30% importance for accessibility); \(w_{3}\) = 0.4 (40% importance for predictive maintenance). Based on these values, the \(MI\) can be computed as in Equation (41):  
\[ \begin{array}{l}MI_{Overall}=\frac{w_{1}\cdot MT+w_{2}\cdot AS+w_{3}\cdot PME }{w_{1}+w_{2}+w_{3}}=\frac{0.3\cdot 84.21+0.3\cdot 92.1+0.4\cdot 21.05}{0.3+0.3 +0.4}\\ =\frac{25.26+27.63+8.42}{0.3+0.3+0.4}=61.28\end{array}  \tag{41}\]  
For the proposed solar tracking prototype, the overall \(MI\) is 61.28. This combined metric provides a single numerical value that reflects the maintainability of the solar tracking system, considering modularity, accessibility, and predictive maintenance facilities with the associated weights. Table 3 shows a comparison between the proposed solar tracking prototype and several other dual-axis designs from related research, in terms of the total number of components, modularity, accessibility, and predictive maintenance efficiency. Related study indicates that solar tracking devices have been developed for a variety of goals, including power gain demonstration [27; 28], and the implementation of predictive monitoring algorithms and test solutions for increased energy output [74; 75]. Regardless of their development target, solar tracking systems must feature a durable and dependable