# A Data-Driven Model of Cable Insulation Defect Based on Convolutional Neural Networks
## 3 Modeling Method of Data-Driven Model
Compared with traditional fully connected networks, convolutional neural networks in which the weights of the convolutional layers are shared use the network to reduce the number of training parameters, reducing the complexity of the network model, reducing overfitting, resulting in better generalization capabilities, and operating with fewer connections and parameters than traditional fully connected networks, making them easier to train. In addition, convolutional neural network systems use shared parameters of connection weights in the same convolutional or pooling layers. These features greatly reduce the number of parameters and reduce the complexity of data training and the risk of training overfitting. Consequently, convolutional neural networks offer significant advantages and powerful feature learning capabilities in building deep structural neural networks [21]. As the number of layers increases, the feature extraction of the convolutional neural network gradually increases, but the number of parameters needed for training also needs to increase, so the demand for sample data also increases, which may also make the sample data will be overfitted [22]. However, there is no mature theoretical framework for the determination of the number of network layers. Therefore, in the actual training process, the optimal structural system needs to be selected through a large number of experiments in order to choose a better performance. ### Data-Driven Model Implementation Based on CNN  
This section evaluates the classification performance of convolutional neural networks, examining the effects of different network layers, different activation functions and different pooling methods on cable data-driven models, and comparing them with back propagation neural network (BPNN). The performance of a convolutional neural network is highly dependent on the number of layers in the network. In this paper, we tested the performance of 1-5 layers of convolutional neural network respectively, using the pooling method of maximum pooling, the activation function using Swish function, the number of convolutional kernels of each layer are 32, and the size of convolutional kernels are all 3 \(\times\) 3. The results are shown in Table 3.  
When the number of layers of the network is reduced, the ability of the convolutional neural network to fit the samples will be reduced. When the number of layers is too large, although the network can extract deeper data features, the information transfer between the layers of the network will cause data wastage and overfitting problems, instead making the training accuracy decrease and the training time increase. As can be seen from Table 3, convolutional neural networks work better at 3 layers. The two mainstream pooling methods are maximum pooling and average pooling, and the maximum pooling method has better recognition accuracy than average pooling because it can extract the most characterizing features. Therefore, the final convolutional neural network architecture used in this paper consists of three convolutional layers and three pooling layers. In addition, the performance of convolutional neural networks was compared with BPNN, and the results are shown in Table 4.  
As can be seen from Table 4, the overall accuracy of the convolutional neural network in both methods reached 94.3%, which is higher than the computational accuracy of BPNN. The process of building a data-driven model based on convolutional neural networks is shown in Figure 2. Firstly, the original data is collected on the distribution of electric field under different operating conditions of the cable, and 11 feature parameters affecting the cable operating condition are constructed to obtain the cable feature dataset. The feature data set is then divided into a training set and a test set, with 80% and 20% of the weight of each respectively.