ARTICIAL INTELLIGENCE BASED WIRELESS SENSOR NETWORK FOR LEAKAGE DETECTION IN LONG RANGE PIPELINES

Abstract

Pipelines are considered to be the most widely used source for transportation of oil/gas worldwide. For the past few decades the incidents of oil and gas pipeline failures are becoming quite frequent, causing financial costs, environmental damages and health hazards. This situation is due to the lack of accurate methods of inspection for oil and gas pipelines. Wireless sensor network facilitates efficient supervising and checking of environment from distant locations reliably. The negative pressure wave (NPW) technique is commonly used for fast leakage detection. The difficulty with the NPW method is the complexity of the analysis of the pressure signatures under high noise scenarios and presence of slow leakages. Hence, a full-proof inspection method is required to provide monitoring of the condition of oil and gas pipeline. In light of the issues of low efficiency and high false alarm rates in traditional pipeline condition monitoring, we propose a wireless sensor monitoring framework which provides a real time parametric view and sufficient information for critical oil and gas pipeline fault/leakage detection. Data is acquired in real-time and processed in order to make decision to predict presence of leakage in pipelines. The real-time nature of the data requires automated decision making than personal inspection. For this purpose an intelligent machine learning based algorithm is designed which can provide considerable accuracy for detection of slow /small leakages in natural gas/oil pipeline monitoring wireless sensor networks (WSN). The information processing algorithms in WSNs are mainly modified techniques from the field of multidimensional data series analysis. We apply the methods of support vector machine (SVM) using optimal kernel functions parameter and Gaussian mixture model (GMM) in multidimensional feature space. The proposed technique is validated using a series of experiments on a field deployed test bed, with regard to performance of detection of leakages in pipelines.