Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach

Abstract

This paper presents an intelligent failure prediction system for oil and gas pipeline using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach. Since the past decade, the incidents of oil and gas pipeline leaks and failures which happened around the world are becoming more frequent and have caused loss of life, properties and irreversible environmental damages. This situation is due to the lack of a full-proof method of inspection on the condition of oil and gas pipelines. Onset of corrosion and other defects are undetected which cause unplanned shutdowns and disruption of energy supplies to consumers. Existing failure prediction systems for pipeline which use non-destructive testing (NDTs) methods are accurate, but they are deployed at pre-determined intervals which can be several months apart. Hence, a full-proof and reliable inspection method is required to continuously monitor the condition of oil and gas pipeline in order to provide sufficient information and time to oil and gas operators to plan and organize shutdowns before failures occur. Permanently installed long range ultrasonic transducers (LRUTs) offer a solution to this problem by providing an inspection platform that continuously monitor critical pipeline sections. Data are acquired in real-time and processed to make decision based on the condition of the pipe. The continuous nature of the data requires an automatic decision making software rather than manual inspection by operators. Support Vector Machines (SVMs) classification approach has been increasingly used in a multitude of domains including LRUT and has shown better performance than other classification algorithms. SVM is heavily dependent on the choice of kernel functions as well as fine tuning of the kernel and soft margin parameters. Hence it is unsuitable to be used in continuous monitoring of pipeline data where constant modifications of kernels and parameters are not unrealistic. This paper proposes a novel classification technique, namely Euclidean-Support Vector Machines (Euclidean-SVM), to make a decision on the integrity of the pipeline in a continuous monitoring environment. The results show that the classification accuracy of the Euclidean-SVM approach is not dependent on the choice of the kernel function and parameters when classifying data from pipes with simulated defects. Irrespective of the kernel function and parameters chosen, classification accuracy of the Euclidean-SVM is comparable and also higher in some cases than using conventional SVM. Hence, the Euclidean-SVM approach is ideally suited for classifying data from the oil and gas pipelines which are continuously monitored using LRUT.