Application of support vector machine for pattern classification of active thermometry-based pipeline scour monitoring

Abstract

Pipeline scour monitoring is becoming one of the key requirements in oil and gas industry. To implement scour monitoring for offshore pipeline, a monitoring system that based on active thermometry is proposed. Our previous investigations have shown that the system has provided many advantages over traditional scour monitoring methods. In this paper, a novel scour automatic detection scheme based on nonlinear curve fitting and support vector machine (SVM) is proposed to realize automatic diagnosis of pipeline scour. On account of the varied heat transfer patterns of a line heat source in sediment and water scenarios, the experimental temperature profiles are nonlinearly fitted to their theoretical models. Features extracted by nonlinear curve fitting can dramatically reduce the dimensions of the data. Subsequently, the extracted features are inputted into SVM classifier to judge where the pipeline is exposed to water or buried in the sediment. In order to evaluate the performance of SVM, SVM with different kernel functions are compared with the back-propagation neural networks, which is the most popular neural network for pattern recognition and classification. Results show that the SVM model with radial basis function kernel outperformed other classification models. Finally, aiming to obtain the optimal heating time of the system, the optimal SVM model is employed to recognize datasets with different heating time. Copyright © 2014 John Wiley & Sons, Ltd.