# Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network
## 4 Conclusions  
The popularity of artificial neural networks as tools for solving classification problems results mainly from the fact that such a network can often make more accurate decisions than a team of experts, and in addition it can be implemented in almost any branch of science. A neural network designed for medical applications by researchers from the U.S. and from China achieves correct patient diagnosis rates often higher than teams of medical experts. For example, correct diagnosis rate for asthma is 90%, while the rate achieved by doctors is within 82-90% [23]. Data from magnetic sensors are well suited for further processing in search for potential damage, and the two-dimensional record mode of the data facilitates the processing and subsequent verification of the processed data. A correct manual recognition of the potential \begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation** & **Correctly Detected** & **Accuracy [\%]** \\ \hline _logsig_ & 647 & 80.88 \\ _radbas_ & 648 & 81.00 \\ _softmax_ & 506 & 63.25 \\ _tansig_ & 660 & 82.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Neural network accuracy. Figure 11: Number of epochs until the end of the learning process. Figure 12: Mean square error. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation** & **Epochs** & **MSE** \\ \hline _logsig_ & 43.40 & 0.0137 \\ _radbas_ & 30.13 & 0.0092 \\ _softmax_ & 34.43 & 0.0261 \\ _tansig_ & 39.15 & 0.0107 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of the network. problems requires a well-trained operator, who identifies the most characteristic points allowing an area to be classified into a particular category. Therefore, application of artificial neural networks for solving such problems may prove advantageous, regardless of the need to search for values which could serve as characteristics for a given category. This is because the network will, during the learning process, determine the input weights in order to evaluate which element of the input sequence is more significant in a given category. The application of artificial intelligence in the form of neural networks to the classification of damage to the conveyor belt seems a promising research direction. The preliminary research results described in this article confirm that the potential of artificial neural networks can be used also in this area. In order to obtain a higher ability of the network to classify damaged areas, adding further input values should be considered (e.g., a shift of the center of gravity of a particular sub-area from the geometric center, modification of the distances between the centers of gravity of individual sub-areas, or even changes of the area span in both directions). The improvement of the results obtained by the designed neural network would certainly be obtained with the introduction of more training data. The use of the increasingly popular deep neural networks would also be worth considering. The development of these networks could be possible by the advancement of computer techniques, including increasing the computing power of computers, parallel data processing, and also thanks to the development of fast computing algorithms. Deep neural networks are able, somehow automatically, to extract diagnostic features for a given class of objects without supervision, thus they excuse the user from the difficult problem of defining and then selecting the key diagnostic features of the examined process [17]. It is worth noting that the selection of neural network parameters has a significant impact on the quality of its operation. As part of the research described here, both the effect of the number of neurons in the hidden layer of the network and the type of activation function used in it were checked. It can be seen that the network achieves the best results for 20 (or 100) neurons in the hidden layer--both the recognition efficiency is the highest, amounting to 82.00 % (or 83.75 %), and the MSE error is the smallest--0.0114 (or 0.0110).