Vibration-Based Discriminant Analysis for Pipeline Leaks Detection

Abstract

Pipelines are useful for transporting liquids from one place to another. The main problem that often occurs in pipelines is leakage which results in production and financial losses. The importance of detecting pipeline leaks makes the industries look for effective detection methods to avoid bigger losses. Several previous studies have proven that the vibration-based method is successful in detecting leaks in pipelines. However, the vibration-based method used in the previous study is relatively complicated and requires specialists to interpret the results. This study proposes a machine learning-based detection method that can classify pipe conditions directly without the help of a specialist. The proposed method is vibration-based discriminant analysis; a machine learning algorithm that recognizes pipeline conditions from their vibration pattern instead of spectrum. The proposed method was tested on a test rig consisting of a closed-loop pipeline equipped with a leak-pipe test segment. The vibration signal is taken using an accelerometer placed on the leak-pipe test segment. Time domain vibration data is extracted using several statistical parameters which aims to reveal information related to pipe conditions. The vibration data collected is divided into two groups, namely training-data and testing-data. The discriminant analysis model is trained to recognize the vibration pattern of the pipeline using training-data and then tested using testing-data. There are four leak sizes introduced in this study, small, medium, and large. Meanwhile, normal condition (no leaks) is used as benchmarking. The study shows that the proposed method is effective in classifying four pipe conditions with the accuracy up to 95%.