## 4 Results  
From Figure 11, the burden surface data were acquired using phased array radar and divided by expert experience into (a) normal, (b) center is too high, (c) funnel is too large, and (d) coke is too much. These four types of surfaces each had 1000 sets of data. Through the classification experiment, the accuracy of the burden surface recognition under each feature was verified. The training and test sets were at a ratio of 7:3.  
The four sets of feature parameters of the burden surface, based on expert experience, are shown in Table 1. These feature parameters can reflect differences among the burden surface. For example, the \(w_{c}\) parameter of the (d) group was significantly larger than that for the other surfaces, indicating that there was too much central coking. Group (c) had an oversized \(w_{f}\) parameter, indicating an oversized funnel area, and so on.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Normal Surface** & **Surface Center That Is Too High** \\ \hline Highest point of the central coke area & (\(-\)0.0149, 0.0603, 8.3764) & (\(-\)0.0078, 0.0371, 8.8423) \\ \hline Center coke area width \(w_{c}\) & 4.4337 & 3.5887 \\ \hline Average height of central coke area \(h_{c}\) & 7.3073 & 8.0187 \\ \hline Lower funnel center \((x_{d},y_{d})\) & (\(-\)0.1030, \(-\)0.0579) & (\(-\)0.3332, 0.0131) \\ \hline Average height of the lower part of the & 6.2381 & 7.1951 \\ funnel \(h_{l}\) & 7.0114 & 5.7969 \\ \hline Upper funnel center \((x_{u},y_{u})\) & (\(-\)0.0665, \(-\)0.1135) & (\(-\)0.0396, \(-\)0.0030) \\ \hline Average height of the upper part of the & 7.1065 & 8.0944 \\ funnel \(h_{u}\) & 7.1065 & 8.0944 \\ \hline Average depth of funnel \(h_{f}\) & 0.8684 & 0.8993 \\ \hline Outer edge center \((x_{o},y_{o})\) & (0.0090, 0.1101) & (\(-\)0.0263, \(-\)0.0171) \\ \hline Outer edge radius of burden surface \(R_{o}\) & 6.1137 & 6.1334 \\ \hline Average height of the outer edge of the & 5.986 & 6.0198 \\ \hline Average width of the platform \(w_{p}\) & 2.6135 & 3.235 \\ \hline
**Category** & **Oversized funnel** & **Oversized central coke area** \\ \hline Highest point of the central coke area & (\(-\)0.0767, \(-\)0.0379, 7.3416) & (\(-\)0.0232, \(-\)0.0657, 8.8423) \\ \(\big{(}c_{x},c_{y},c_{z}\big{)}\) & 4.1924 & 7.9719 \\ \hline Center coke area width \(w_{c}\) & 4.1924 & 7.9719 \\ \hline Average height of central coke area \(h_{c}\) & 6.6158 & 7.1492 \\ \hline Lower funnel center \((x_{d},y_{d})\) & (\(-\)0.0232, \(-\)0.0657) & (\(-\)0.0109, 0.1026) \\ \hline Average height of the lower part of the & 5.89 & 5.8666 \\ funnel \(h_{l}\) & 11.1124 & 9.9707 \\ \hline Funnel width \(w_{f}\) & 11.1124 & 9.9707 \\ \hline Upper funnel center \((x_{u},y_{u})\) & (0.0014, \(-\)0.0049) & (0.4436, \(-\)0.0149) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Part of the feature parameters.  
The feature parameters contain shape information of the burden surface, such as the depth, width, and radius. Based on this underlying mathematical shape information, specious surface shapes that are difficult to recognize in neural networks can be excluded. The center coke area, funnel area, and platform area contain their own feature parameters to facilitate a quick search of the fault area. In contrast to the depth features (voxel features and point cloud features), each feature parameter has mathematical significance and is solvable.  
The ordinate represents the surface category, and the abscissa is the test set designator. The accuracy of the test set was about 65.37%. From the classification results of the feature parameters, the recognition accuracy of (a) and (c) was low. This was due to the similarities between the two sets of surfaces, which made accurate classification difficult from a data perspective. Therefore, it was necessary to extract the voxel features and the point cloud features.