A novel oil pipeline leakage detection method based on the sparrow search algorithm and CNN

A novel sparrow search algorithm and convolutional neural network (SSA-CNN) method is proposed for oil pipeline leakage detection. Firstly, the proposed SSA-CNN method converts the input data from time series to two-dimensional matrix, and the classification conditions of different convolution kernel sizes and different pooling sizes are compared. Then, the SSA algorithm is used to optimize the parameters of the CNN. The simulation results show that, compared with the traditional machine learning method, using two-dimensional data as input can enhance the neural network's extraction of eigenvalues. The proposed SSA-CNN method was able to accurately classify 148 sample points in 150 test sets with an accuracy rate of 98.67%, which is not only higher than traditional machine learning methods, but also further improves the classification capability of CNN, while the SSA-CNN method can use the parameters already learned in the test set to ensure real-time detection.

Automated summary

A novel SSA-CNN method is proposed for oil pipeline leakage detection, which converts input data to two-dimensional matrix and optimizes CNN parameters using SSA algorithm, enhancing the neural network's extraction of eigenvalues. Experimental results show an accuracy rate of 98.67%.