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FSM - A UNIQUE METHOD FOR MONITORING CORROSION OF STEEL PIPING AND VESSELS

Roe D. Strømmen, Harald Horn, Kjell Wold

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Abstract

This article describes the principles and typical applications of FSM-The Electric Fingerprint Method. This is a new technique based on a patented principle, developed for the purpose of detecting and monitoring general and localized corrosion, erosion, or abrasion, and cracking in steel and metal structures, piping systems, and vessels. Results are presented from laboratory tests and early data from field installation with Shell U.K. Exploration and Production's Dunlin «A» platform in the North Sea

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What this paper is about

This article describes the principles and typical applications of FSM-The Electric Fingerprint Method. This is a new technique based on a patented principle, developed for the purpose of detecting and monitoring general and localized corrosion, erosion, or abrasion, and cracking in steel and metal structures, piping systems, and vessels. Results are presented from laboratory tests and early data from field installation with Shell U.K. Exploration and Production's Dunlin «A» platform in the North Sea

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This article describes the principles and typical applications of FSM-The Electric Fingerprint Method. This is a new technique based on a patented principle, developed for the purpose of detecting and monitoring general and localized corrosion, erosion, or abrasion, and cracking in steel and metal structures, piping systems, and vessels. Results are presented from laboratory tests and early data from field installation with Shell U.K. Exploration and Production's Dunlin «A» platform in the North Sea

Key concepts: Piping, Engineering, Corrosion monitoring, Corrosion, Abrasion (mechanical), Cracking, Metallurgy, Forensic engineering

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FSM - A UNIQUE METHOD FOR MONITORING CORROSION OF STEEL PIPING AND VESSELS — Research Paper | ScholarLens