Dissolved Gas Analysis (DGA) is one of the important methods for determining transformer internal faults.Aiming at the shortcomings of the traditional shallow⁃based machine learning method in transformer fault diagnosis in feature extraction and generalization ability, a transformer fault diagnosis method based on Convolutional Neural Network (CNN) was proposed. The convolution layer in the network was used for feature conversion of dissolved gas in oil, and fault sensitive features were extracted by combining with the ability of pooling layer to strengthen important features. The effects of the number of convolution kernels, the size of convolution kernels, pooling layer and network depth on the diagnostic performance of the model were studied experimentally. The models of Convolutional Neural Network, Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) were compared and analyzed by confounding matrix, ROC curve and PR curve. The experimental results show that the Convolutional Neural Network model has better diagnostic performance.

The reliability of pressure pipeline containing defects in engineering is very important. The pipeline of the oil industry may contain types of potential defects. It should be considered the defected size, the uncertainty of the loaded parameters, so introduced the Monte Carlo method to use random sampling for these uncertain parameters. Monte Carlo method is based on the probability and statistic theory method, it can realistically describe the characteristics of the random things, and overcome the disadvantage of the deterministic failure analysis method, more accurate calculate the failure probability of the pipeline, it provides a technical means for diagnosing system security of petrochemical enterprise.