Deep Learning Application as a Method for Classification Equipment Failure on Natural Gas Pipeline

Abstract

Predicting maintenance and preventing failures in natural gas pipelines is critical for safety and efficiency in the oil and gas industry. This research utilizes machine learning (ML) and deep learning (DL) methods to predict failures in natural gas transmission pipelines. Using a dataset from the U.S. Department of Transportation’s Pipeline and Hazardous Materials Safety Administration (PHMSA), the study examines failure incidents from 2010-2019 and non-failure data from 2020-2023. The research involves comprehensive data preprocessing, addressing missing values and dataset imbalances. Key pipeline attributes such as diameter, wall thickness, age, and operating pressure are used for failure prediction. The ML approach employs logistic regression, optimized for binary classification tasks, to establish the probability of pipeline failures. For the DL approach, an Artificial Neural Network (ANN) with Feed Forward and Back Propagation algorithms is developed. Key parameters, including activation functions and hyperparameters, are finely tuned to enhance predictive performance. ReLU activation functions are used in input and hidden layers, while the sigmoid function is applied to the output layer. Performance evaluation of both ML and DL models is based on accuracy, F1 Score, and Area Under Curve (AUC). In summary, ML showed better performance than DL, with value of accuracy is 0.74, F1 Score is 0.85, and AUC is 0.55 while the value of accuracy, F1 score, AUC from DL is 0.61, 0.003, and 0.58 respectively. DL offers superior performance in handling complex and huge amounts of data. In the other hand, ML offers more robust models and simpler adjustable models than DL. Because of the amount of computed data in this research, it is better to have ML as a predictive algorithm.