## 5 Results and Discussion
### Discussion  
The proposed approaches provide the most efficient CIs to be evaluated for crack detection. The results in Tables 3, 5, 7, 9 and 10 show the approach 1 achieves an accuracy rate of more than 91% from the seventh CI for all data sets while approach 2 achieves a rate of more than 93% from the fifth CI. Furthermore, the \(std\) obtained by each K-fold is presented in these tables where it can be seen that new CIs help reducing the \(std\) of the cross-validation process. This result indicates the classifier becomes more robust to new features. Note that using the \(std\) metric from the cross-validation process is a more useful, unlike the typical single train/test experiment. In Figure 5, the trend of the curves indicates a fast growth of the accuracy up to the seven CIs, later the value of the accuracy flattens, i.e. the classifier will not significantly improve the accuracy after adding more CIs. It is important to note that all trends present a continuous increment in the accuracy as CIs increases except for CI_FL, for which the fourth CI decreases its performance. Figure 8 shows the maximum classification accuracy of each data set and the number of indicators for which it was obtained. Approach 1, which employs a single accelerometer, achieves the best results by combining the CIs of time + frequency domain for both the left and right side accelerometers. Slightly better classification results are achieved with approach 2; however, data fusion by using six- accelerometers may involve greater challenges to perform the system diagnostic. For the three domains analyzed, better results are achieved with the right-side accelerometers, which may be due to the fact that the drive is not on this side and the signal information is less noisy. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{4}{c}{**CI\_DFRL**} \\ \hline \# & **CIs** & **Domain** & **Sensor** & **Accuracy (\%)** & **Std** \\ \hline
**1** & MDF & Freq & LV & 25.5 & 0.19 \\
**2** & VCF & Freq & RL & 63.47 & 3.96 \\
**3** & CP2 & Freq & RA & 88.24 & 1.12 \\
**4** & SSC & Time & LV & 92.68 & 1.67 \\
**5** & CP2 & Freq & LL & 94.21 & 1.87 \\
**6** & PKF & Freq & LV & 96.38 & 1.24 \\
**7** & PKF & Freq & RL & 97.02 & 0.82 \\
**8** & CP2 & Freq & RL & 97.2 & 1.14 \\
**9** & ZC & Time & LL & 97.38 & 0.38 \\
**10** & Skewnessf & Freq & LV & **98.37** & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Top 10 features and accuracies average over time and frequency domain using all sensors, CI_DFRL. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**CIs**} & \multirow{2}{*}{**Domain**} & \multirow{2}{*}{**Sensor**} & \multicolumn{3}{c}{**Classes (Accuracy\%)**} \\ \cline{6-8}  & & & & **1** & **2** & **3** & **4** \\ \hline
**1** & MDF & Freq & LV & 100 & 0 & 0 & 0 \\
**2** & VCF & Freq & RL & 70.94 & 46.94 & 25.82 & 39.88 \\
**3** & CP2 & Freq & RA & 84.4 & 64.75 & 66.18 & 61.97 \\
**4** & SSC & Time & LV & 90.76 & 84.72 & 79.64 & 93.65 \\
**5** & CP2 & Freq & LL & 96.46 & 94.29 & 85.82 & 98.13 \\
**6** & PKF & Freq & LV & 96.8 & 93.93 & 86.55 & 97.77 \\
**7** & PKF & Freq & RL & 97.53 & 96.44 & 93.09 & 98.88 \\
**8** & CP2 & Freq & RL & 96.83 & 96.09 & 94.18 & 99.62 \\
**9** & ZC & Time & LL & 97.88 & 97.51 & 96.36 & **100** \\
**10** & Skewnessf & Freq & LV & 98.58 & 97.86 & 96.73 & 98.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Accuracy per class to CI_DFRL. Regarding the other works in this same case, [PERSON] et al. [12] reached a maximum accuracy of 96.43% and [PERSON] et al. [7] achieved the overall probability of detection at 95%, with 32 features (energy computed on WPT). In this work, the maximum accuracy is 97.14% with the approach 1, and 98.37% with the approach 2. The ZC and SCC condition indicators of time domain and PKF, CP2 in frequency domain, are the common CIs among the top 10 CIs in both approach 1 and 2.