Multidimensional data statistical processing of magnetic flow leakage signals from a Colombian gas pipeline. (November 2021)

• Record Type: Journal Article
• Authors: Mujica, Luis Eduardo; Ruiz, Magda; Villamizar, Rodolfo
• Is Part Of: Structural health monitoring. Volume 20:Number 6(2021)
• Page Start: 2963
• Page End: 2992
• Publication Date: 2021-11
• Subjects: Gas pipeline -- multiway principal component analysis -- multidimensional signal processing -- smart in-line inspection tool -- weld detection -- damage detection; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921720977393
• Languages: English
• ISSNs: 1475-9217

Abstract

The hydrocarbon industry in Colombia is one of the principal pillars for the Colombian economy, representing around 5% of its gross domestic product. Since petroleum reserves have decreased, gas becomes one main alternative for economical growth. However, current gas pipelines have been in service for over 30 years and some of them are buried and phenomena, such as metal losses, corrosion, mechanical stress, strikes by excavation machinery, and another type of damages, are presented. The maintenance program of these structures is typically corrective type and is very expensive. To overcome this situation, the native research institute "Research Institute of Corrosion—Corporación para la Investigación de la Corrosión" recently developed an in-line inspection tool to be operated in Colombian gas pipelines to get valuable information of their current state along thousands of kilometers. A huge quantity of data is recorded (including tool movement, magnet, magnetic flow leakage, and caliper signals), which demand a high-computational cost and an adequate tool analysis to establish the current pipeline structural health condition. In this sense, authors have shown in several works that principal component analysis is an effective tool to detect and locate abnormal operational structural conditions from multidimensional data. In a previous analysis, multidimensional data were used to locate possible damages along the pipeline. However, most of the activated points belonged to weld points. Then, in this article, it is proposed to use the root mean square value of magnetic flux leakage signals to separate these points and to obtain sets of signals by sections removing the welds, and then multiway principal component analysis is applied for each set of signals of each gas pipeline section. The maximum values of damage indices ( Q andT 2 -statistics) of each section are conserved to activate the sections of the gas pipeline with more probability of damages and then, they must be evaluated by experts.