## 7 The Experimental Results and Discussion
### The Reconfigurability-Reliability Dependency Based on the Improved Fault Coverage  
While initial dependability evaluation studies have demonstrated the efficiency of fault coverage-aware metrics [66] in computing two quality parameters, STF and SRF, based on software [62], hardware [63; 64], and in-circuit (IC) [65] testing strategies, further investigations have revealed the possibility of combining these verification methods into one unified metrics system [68]. The improved reliability-oriented metrics were then applied to a solar-powered home automation setup [69] to show the impact of software, hardware, and IC errors on the global energy production of modern solar tracking systems. Moreover, using specific error correction codes (ECCs), such as Hamming codes, may effectively increase the SRF of an automated PV panel by removing single bit-flip and stuck-at faults from the system [79]. Thus, the research activity was primarily focused on analyzing and increasing the dependability of dual-axis solar trackers, without consideration for their maintainability characteristics. To bridge this gap, our work exploits the reconfigurability-reliability dependency resulting from enhanced fault coverage to develop a predictive failure curve for evaluating the maintainability of modern solar tracking systems. For our case study, we will focus on the P-F curve analysis of the solar tracker's electrical equipment, specifically the MCU's functionality under various fault scenarios comprising stuck-at-0 (Sa0) and stuck-at-1 (Sa1) defects. The test cases were divided into multiple batches according to the severity of the Enable (e), Relay (r), Step (s), and Direction (d) pin damage, namely, single, double, and triple SaFs. The fault scenarios, on the other hand, were evaluated using the LFSR unit presented in Figure 10 and the RBILBO design illustrated in Figure 11. The experimental results were obtained from a testbench module that was simulated in the Altera Modelsim environment. Regarding single SaFs, the diagnostic system identified 12 Sa0 and Sa1 faults out of 32 test scenarios using only one polynomial function (\(G1\)), outlining an initial FC of 37.5%. After the second test batch, when the polynomial function of the proposed RBILBO architecture was switched to \(G2\), 27 samples were recognized as Sa0 and Sa1 faults, increasing FC by 46.87%. By simulating a third batch of test scenarios, the fault diagnostic system was able to identify all Sa0 and Sa1 faults via the \(G3\) polynomial function, further increasing the FC to 100%. The final FC can be written as in Equation (27):  
\[ \begin{array}{l}F\text{C}_{{SSaF}}=F\text{C}_{{SaF1}}(G_{1})+F\text{C}_{{SaF1 }}(G_{2})+F\text{C}_{{SaF1}}(G_{3})\\ =37.50\%+46.87\%+15.63\%=100\%,\end{array}  \tag{27}\]  
where \(F\text{C}_{{SSaF}}\) represents the cumulative FC for single SaFs, \(F\text{C}_{{SaF1}}\) denotes the individual fault coverage for single SaFs, and, respectively, \(G_{1}\), \(G_{2}\), and \(G_{3}\) designate the generator polynomial functions used in the RBILBO. In the second batch of SaFs, the fault diagnostic approach identified 12 Sa0 and Sa1 out of 24 test instances, using the \(G1\) polynomial function, yielding an initial fault coverage of 50%. After utilizing the second set of test vectors and adjusting the polynomial to \(G2\), the initial fault coverage increased to 79.16%, reducing aliasing by 29%. Furthermore, by performing an additional set of 24 test vectors with the \(G3\) polynomial function, we were able to increase fault coverage by 20.8% while lowering aliasing to 0%. The mathematical relation for the final FC is presented in Equation (28):  
\[ \begin{array}{l}F\text{C}_{{DSaF}}=F\text{C}_{{SaF2}}(G_{1})+F\text{C}_{{SaF2 }}(G_{2})+F\text{C}_{{SaF2}}(G_{3})\\ =50\%+29.16\%+20.84\%=100\%,\end{array}