## 7 Conclusions and Future Work  
Aiming at the problems of high difficulty, high risk, and low recognition efficiency in the existing artificial detection methods for fault detection in BTRs, a health monitoring method for the balancing tail ropes of a hoisting system based on a convolutional neural network is proposed in this paper. In this method, the real-time tail rope images are first captured through CCD cameras and the data transmission is realized using a movable sensor network in the vertical shaft. Then, the preprocessed images are input to train the convolutional neural network in order to realize the automatic recognition of the BTR faults. Finally, fault warnings are made based on the identification results. The research can be summarized and concluded as follows: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Description** & **Accuracy** & **Time Cost** \\ \hline KNN & Using ball-tree structure & 94.04\% & 50 s \\ ANN-BP & Structure is 784–200–64–32–9 & 96.40\% & 1 s/epoch, 385 us/step \\ Proposed & Structure is Input (28 \(\times\) 28)–64C(3 \(\times\) 3)–64P(2 \(\times\) 2)–128C(3 & 100\% & 32 s/epoch, 10 ms/step \\ CNN & \(\times\) 3)–128P(2 \(\times\) 2)–FC(200–64–32)–Output(9) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The different algorithm results. (1) A CNN including two convolution layers, two pooling layers, and three fully connected layers is proposed. The structure of the CNN is denoted as Input(28 \(\times\) 28)-64C(3 \(\times\) 3)-64P(2 \(\times\) 2)-128C(3 \(\times\) 3)-128P(2 \(\times\) 2)-FC(200-64-32)-Output(9), meaning that the dimensions of the input 2D data are 28 \(\times\) 28; the CNN first applies 1 convolutional layer with 64 filters and the filter size is 3 \(\times\) 3. Then, one maximum-pooling layer with pooling size 2 \(\times\) 2 is used. One convolutional layer with 128 filters (filter size is 3 \(\times\) 3) is applied next, after which one pooling layer whose pooling size is 2 \(\times\) 2 is applied. Finally, three fully connected layers whose hidden neuron numbers are 200, 64, and 32, respectively, are applied. The size of the output layer is 9, which is equal to the number of fault types. (2) A method for the description and establishment of an image dataset that can cover the entire feature space of overhanging BTRs is proposed. The BTRs image dataset covering the 9 features in the state space is set up and further expanded to a scale of 4500 by scale and rotation to enhance the generalization ability of the network model. The same method can be used to describe data from hoisting systems containing more than two tail ropes. (3) The CNN, KNN, and ANN-BP algorithms were used to train and test the established tail rope image dataset, and the effects of the hyper-parameters of the network diagnostic accuracies were investigated experimentally. The experimental results showed that the feature of the BTR image was adaptively extracted by the CNN's convolutional and pooling operations, which means that a great deal of manpower can be saved and online updates can be realized, so as to meet real-time requirements. The learning rate and batch size seriously affected the accuracy and training efficiency, with the better values of the learning rate and batch size being 0.01 and 5, respectively. The \(L_{2}\) regularization process of the fully connected layer F1 could prevent overfitting. The fault diagnosis accuracy of CNN was 100%, while that of KNN was 94.04% and that of ANN-BP was 96.40%, so the diagnosis accuracy of CNN was much higher than that of the KNN and ANN-BP algorithms. Additionally, CNN could accurately identify and predict all kinds of BTR states, while ANN-BP and KNN had some deficiencies in distinguishing similar fault types. Therefore, the CNN had high accuracy, real-time functioning, and a good generalization performance, which are more suitable for application in the health monitoring of hoisting system BTRs.