## 7 The Experimental Results and Discussion
### The Reconfigurability-Reliability Dependency Based on the Improved Fault Coverage
\begin{array}{l}STF_{SaF_{1}}(G_{1})=\frac{DR_{SaF_{1}}(G_{1})}{2^{D}}= \frac{12}{2^{5}}=0.375\\ STF_{SaF_{2}}(G_{1})=\frac{DR_{SaF_{2}}(G_{1})}{2^{D}}=\frac{16}{2^{5}}=0.5\\ STF_{SaF_{3}}(G_{1})=\frac{DR_{SaF_{3}}(G_{1})}{2^{D}}=\frac{15}{2^{5}}=0.468 \end{array} \right., \tag{33}\]  
where \(D=5\) is the number of DFFs used in the RBILBO design. By adding the G2 polynomial, we obtain the following:  
\[\left\{ \begin{array}{l}STF_{SaF_{1}}(G_{1},G_{2})=\frac{DR_{SaF_{1}}(G_{1}G _{2})}{2^{D}}=\frac{26.99}{2^{3}}=0.843\\ STF_{SaF_{2}}(G_{1},G_{2})=\frac{DR_{SaF_{2}}(G_{1}G_{2})}{2^{D}}=\frac{25.33} {2^{3}}=0.791\\ STF_{SaF_{3}}(G_{1},G_{2})=\frac{DR_{SaF_{3}}(G_{1}G_{2})}{2^{D}}=\frac{25.99 }{2^{3}}=0.812\end{array} \right., \tag{34}\]  
where \(D=5\) is the number of DFFs used in the RBILBO design. By using the \(G_{3}\) polynomial, we compute the following:  
\[\left\{ \begin{array}{l}STF_{SaF_{1}}(G_{1},G_{2},G_{3})=\frac{DR_{SaF_{1}}(G_ {1}G_{2}G_{3})}{2^{D}}=\frac{32}{2^{3}}=1\\ STF_{SaF_{2}}(G_{1},G_{2},G_{3})=\frac{DR_{SaF_{2}}(G_{1}G_{2}G_{3})}{2^{D}}= \frac{32}{2^{3}}=1\\ STF_{SaF_{3}}(G_{1},G_{2},G_{3})=\frac{DR_{SaF_{3}}(G_{1}G_{2}G_{3})}{2^{D}}= \frac{30}{2^{3}}=0.937\end{array} \right., \tag{35}\]  
where \(D=5\) is the number of DFFs used in the RBILBO design. At this point, all computed STF parameters will be used to determine the \(SRF\) of the automated PV panel, according to Equation (36):  
\[SRF=e^{-\frac{DR}{2^{D}}}=e^{-STF}=\frac{1}{e^{STF}} \tag{36}\]  
Similar to the \(STF\) calculations, the \(SRF\) parameter can be determined by inserting the computed values into the global reliability equation system presented in Equation (37):  
\[\left\{ \begin{array}{l}SRF_{G}(G_{1})=e^{-STF_{G}(G1)}=\frac{1}{e^{STF_{G}(G 1)}}=\frac{1}{e^{24\frac{DR}{2^{D}}}}=0.6407\\ SRF_{G}(G_{1},G_{2})=e^{-STF_{G}(G1,G2)}=\frac{1}{e^{24\frac{DR}{2^{D}}}}= \frac{1}{e^{24\frac{DR}{2^{D}}}}=0.4434\\ SRF_{G}(G_{1},G_{2},G_{3})=e^{-STF_{G}(G1,G2,G3)}=\frac{1}{e^{24\frac{DR}{2^{D }}}}=\frac{1}{e^{20\frac{DR}{2^{D}}}}=0.3767\end{array} \right., \tag{37}\]  
where \(SRF_{G}\) is the global reliability factor relative to polynomial functions \(G_{1}\), \(G_{2}\), and \(G_{3}\); \(STF_{G}\) is the algebraic sum obtained from Equations (33)-(35); and \(e\) is [PERSON]'s constant with a standard value of 2.71. In reliability theory, the P-F curve is a graphical model for visualizing an asset's status over time, thereby introducing the notion of condition-based maintenance and other related topics. The P-F curve illustrates the link between asset condition and time. The model shows that from the outset of a failure, the asset condition decreases until it achieves functional failure. The overall purpose of asset inspections in a predictive maintenance plan is to detect condition degradation before the equipment achieves functional breakdown [81]. Figure 13 illustrates the P-F curve generated by an Altera Modelsim simulation spanning 25,000 picoseconds (2.5 \(\times\) 10\({}^{-8}\) s). It is separated into three main regions. The first region, called the predictive domain, is the area where the solar tracking system's normal behavior is continuously monitored for potential hardware faults that may affect the stability of the device. However, over time, we could estimate the first potential failure point, denoted with P, which is provided by the FC determined via the \(G1\) polynomial function. Potential failure indicates the point at which we notice the solar tracker's reliability is starting to decrease, highlighting the second region of the graphical chart designated as the preventive domain. In preventive maintenance, routine inspections, servicing, repairs, and replacements are carried out on equipment regardless of whether there are any signs of impending failure. Despite the efforts to maintain the solar tracker's performance, unidentified hardware faults may continue to damage the system until a median failure point is detected via the G2 polynomial function triggering the third region called the reactive domain.