Aidin Ghavamian

Bio: Aidin Ghavamian is an academic researcher from Universiti Putra Malaysia. The author has contributed to research in topics: Corrosion & Galvanic corrosion. The author has an hindex of 3, co-authored 3 publications receiving 111 citations.

A pressure-based method for monitoring leaks in a pipe distribution system: A Review

Abstract: Leakage from a pipe network possibly poses significant environmental destruction and economic losses due to the release of potential energy. While the pipe network may be planned and constructed to satisfy the requirements of rigorous conditions, it is quite hard to avoid the subsequent appearance of leakages in a pipeline during the system's lifetime. Pressure leak detection enables a fast and reliable action response which is necessary to minimise the damage. Many leak detection approaches have been previously suggested. These methods basically depend on numerical modelling and transient analysis, such as inverse transient analysis, time domain analysis and frequency domain analysis, the negative pressure method, etc. Many methods build upon the analysis of the variation of measured pressure, such as the pressure residual vector method. Hydraulic leak detection has the important advantage of being less costly and has a faster response compared to other leak detection approaches. In this work, various leak detection methods based on pressure are listed and the analysis is reviewed. Both steady state and unsteady state conditions are discussed. The advantages and disadvantages of each approach are mentioned. In addition, methods are included that are suitable for use in both the oil and water industries.

Detection, Localisation and Assessment of Defects in Pipes Using Guided Wave Techniques: A Review.

Abstract: This paper aims to provide an overview of the experimental and simulation works focused on the detection, localisation and assessment of various defects in pipes by applying fast-screening guided ultrasonic wave techniques that have been used in the oil and gas industries over the past 20 years. Major emphasis is placed on limitations, capabilities, defect detection in coated buried pipes under pressure and corrosion monitoring using different commercial guided wave (GW) systems, approaches to simulation techniques such as the finite element method (FEM), wave mode selection, excitation and collection, GW attenuation, signal processing and different types of GW transducers. The effects of defect parameters on reflection coefficients are also discussed in terms of different simulation studies and experimental verifications.

Introduction to the Finite Element Method

Abstract: This chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems

Abstract: In many water distribution systems, a significant amount of water is lost because of leakage during transit from the water treatment plant to consumers. As a result, water leakage detection and localization have been a consistent focus of research. Typically, diagnosis or detection systems based on sensor signals incur significant computational and time costs, whereas the system performance depends on the features selected as input to the classifier. In this paper, to solve this problem, we propose a novel, fast, and accurate water leakage detection system with an adaptive design that fuses a one-dimensional convolutional neural network and a support vector machine. We also propose a graph-based localization algorithm to determine the leakage location. An actual water pipeline network is represented by a graph network and it is assumed that leakage events occur at virtual points on the graph. The leakage location at which costs are minimized is estimated by comparing the actual measured signals with the virtually generated signals. The performance was validated on a wireless sensor network based test bed, deployed on an actual WDS. Our proposed methods achieved 99.3% leakage detection accuracy and a localization error of less than 3 m.