Detection of Corrosion under Insulation Risks with Fiber Optic Technology

Abstract This paper describes a disruptive continuous monitoring system to detect Corrosion Under Insulation (CUI) risks for every meter of pipeline over large distances. Distributed Fiber Optic Sensing (DFOS) has emerged as a viable non-destructive ATEX-proof solution to detect CUI. The system uses a fiber optic cable applied at the outside of the cladding. From fiber optic readings and dedicated algorithms, the location and amount of corrosion are detected and localized with a spatial precision of 0.5 m. To evaluate the system effectiveness, plant operators conducted blind tests. The system was subjected to multiple blind tests, including a 4 km stretch of piping in a petrochemical plant. Field tests were performed over multiple years in complex networks featuring varying heights, insulation materials and pipe sizes. The system was also used to detect CUI on a large storage tank. On every test location during the four test projects, the system detected locations affected by corrosion that were not detected with visual inspections. False positives and negatives remain minimal. With DFOS-technology, manual inspections can be much more targeted and reduced in cost, as there are clear indications where to inspect (and where not). Impact is fewer incidents and downtime while reducing the operational costs on CUI inspection and maintenance.

Authors

Publication Details

Published
2025-04-06
DOI
https://doi.org/10.5006/c2025-00265
Primary Topic
Advanced Fiber Optic Sensors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detection of Corrosion under Insulation Risks with Fiber Optic Technology

T. Van Hoestenberghe, Daan Renders, Niels De Vleeschouwer, Arnout Roukaerts et al.
2 links
Advanced Fiber Optic Sensors
article

Detection of Corrosion under Insulation Risks with Fiber Optic Technology

T. Van Hoestenberghe, Daan Renders, Niels De Vleeschouwer, Arnout Roukaerts, Timothy Van Caelenberghe
article en

Abstract

Abstract This paper describes a disruptive continuous monitoring system to detect Corrosion Under Insulation (CUI) risks for every meter of pipeline over large distances. Distributed Fiber Optic Sensing (DFOS) has emerged as a viable non-destructive ATEX-proof solution to detect CUI. The system uses a fiber optic cable applied at the outside of the cladding. From fiber optic readings and dedicated algorithms, the location and amount of corrosion are detected and localized with a spatial precision of 0.5 m. To evaluate the system effectiveness, plant operators conducted blind tests. The system was subjected to multiple blind tests, including a 4 km stretch of piping in a petrochemical plant. Field tests were performed over multiple years in complex networks featuring varying heights, insulation materials and pipe sizes. The system was also used to detect CUI on a large storage tank. On every test location during the four test projects, the system detected locations affected by corrosion that were not detected with visual inspections. False positives and negatives remain minimal. With DFOS-technology, manual inspections can be much more targeted and reduced in cost, as there are clear indications where to inspect (and where not). Impact is fewer incidents and downtime while reducing the operational costs on CUI inspection and maintenance.

Openalex Percentile: Top 94%
Advanced Fiber Optic Sensors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Detection of Corrosion under Insulation Risks with Fiber Optic Technology — T. Van Hoestenberghe, Daan Renders, et al. · (2025) | TGRS Research Map | TGRS