A Simple and Feasible Detection of Pipe/Tank-Wall Thinning and Corrosion Using Piezoceramic Transducers

A Simple and Feasible Detection of Pipe/Tank-Wall Thinning and Corrosion Using Piezoceramic Transducers

Petroleum industry

Pipelines

Pipeline corrosion

Petroleum transportation

Tanks (containers)

Cost effectiveness

Accident prevention

Piping systems

Steel corrosion

Steel pipe

Frequency domain analysis

Thickness measurement

Transducers

Empirical mode decomposition

It is well known that corrosion drastically shortens the lifespan of pipelines. Pipe wall thinning and leakage due to corrosion are important safety concerns for pipes used in oil, petrochemical, and power plant industries. Cross-sectional irregularity, such as eccentricity or local wall thinning, which normally develops due to either process restrictions or extensive usage, is a prevalent problem in such plants. This paper introduces the feasibility of detecting pipelines and tanks thinning damage using piezo-ceramic transducers (PZCs) resonating at 2MHz. Time and frequency processing were used in combination with the Empirical Mode Decomposition (EMD) as a signal processing method in different conditions. A steel box and a piece of steel pipe, as items under test (IUTs), were set up to imitate the real industrial tanks and pipes used in the petroleum industries. The box was exposed to sulfuric acid as a corrosive substance and parts of the steel pipe were shaved from the inside to simulate the corroded areas. The PZCs were installed on the proper parts of the IUTs and thickness measurements were performed and compared with the results obtained from a standard commercial thickness gauge, Elcometer205. The results show less than a 1% error in thickness compared with the standard equipment when a frequency domain is combined with EMD. Furthermore, the proposed system shows much more simplicity and cost-effectiveness than the standard one. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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Eftekhari, Ali

Baghaei Nejad, Majid

Shahrokh Abadi, Mohammad Hadi

Sensing and Imaging

2023

journalArticle

15572064

10.1007/s11220-023-00415-8

http://dx.doi.org/10.1007/s11220-023-00415-8