A framework for corrosion assessment in metallic structures, from data analysis to risk based inspection

• Autorzy: Cui Xiaofei , Liang Xiaoxia , Bharadwaj Ujjwal

Identyfikatory

DOI

10.17531/ein.2021.1.2

• Języki publikacji: EN

Abstrakty

EN

Metallic corrosion is a big challenge affecting many sectors in a nation’s economy. Necessary corrosion prevention actions have to be taken in order to maintain the integrity of engineering assets susceptible to corrosion. This paper proposes a holistic framework to support the management of corrosion in metallic structures. It is a fully automation corrosion assessment process, with risk updated by Bayesian theory. Through analyzing the thickness data measured by non-destructive testing (NDT) techniques, the influence of corrosion on the component can be estimated using statistical methods, which will enable users to make decisions on maintenance based on quantitative information. A case study using corrosion data from a steel bridge is included to demonstrate the proposed framework. It improved the conventional corrosion analysis method by the proposed statistical approach using representative thickness data, which aims to take full use of the remaining life. This model can be adapted to a wide range of metallic structure suffering from corrosion damage.

Słowa kluczowe

EN

corrosion estimation; steel bridge; metallic structures; risk based inspection; software

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2021
• Tom: Vol. 23, no. 1
• Strony: 11--20
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Cui Xiaofei

• TWI Ltd, Granta Park, Great Abington, Cambridge, CB21 6AL, United Kingdom

autor

Liang Xiaoxia

• TWI Ltd, Granta Park, Great Abington, Cambridge, CB21 6AL, United Kingdom

autor

Bharadwaj Ujjwal

• TWI Ltd, Granta Park, Great Abington, Cambridge, CB21 6AL, United Kingdom

Bibliografia

• 1. American Petroleum Institute. A.P.I. 581, Risk-based Inspection Methodology. Third Edition.2016.
• 2. Anes-Arteche F, Yu K, Bharadwaj U et al. Challenges in the application of DCVG-survey to predict coating defect size on pipelines. Materials and Corrosion-Werkstoffe und Korrosion 2017; 68(3): 329-337, https://doi.org/10.1002/maco.201608917.
• 3. Anes-Arteche F, Bharadwaj U, Yu K, Lee C. Correlation of Pipeline Corrosion and Coating Condition with ECDA Survey Results. in EUROCORR, 2016.
• 4. API. Fitness-For-Service (API 579 Second Edition), Part 5 Assessment of Local Metal Loss. 2007.
• 5. Bharadwaj U R. Risk based life management of offshore structures and equipment. 2010.
• 6. Bin Muhd Noor N N, Yu K, Bharadwaj U, Gan T-H. Making use of external corrosion defect assessment (ECDA) data to predict DCVG% IR drop and coating defect area. Materials and Corrosion-Werkstoffe und Korrosion 2018; 69(9): 1237-1256, https://doi.org/10.1002/maco.201810085.
• 7. Chikobvu D, Chifurira R. Modelling of extreme minimum rainfall using generalised extreme value distribution for Zimbabwe. South African Journal of Science 2015; 111(9-10): 01-08, https://doi.org/10.17159/sajs.2015/20140271.
• 8. Damodaran A. Strategic risk taking: a framework for risk management. Pearson Prentice Hall: 2007.
• 9. Dorafshan S, Maguire M, Collins W. Infrared thermography for weld inspection: feasibility and application. Infrastructures 2018; 3(4): 45, https://doi.org/10.3390/infrastructures3040045.
• 10. Ghanooni-Bagha M, Shayanfar M A, Reza-Zadeh O, Zabihi-Samani M. The effect of materials on the reliability of reinforced concrete beams in normal and intense corrosions. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2017; 19 (3): 393–402, http://dx.doi.org/10.17531/ein.2017.3.10.
• 11. Kalantarnia M, Khan F, Hawboldt K. Dynamic risk assessment using failure assessment and Bayesian theory. Journal of Loss Prevention in the Process Industries 2009; 22(5): 600-606, https://doi.org/10.1016/j.jlp.2009.04.006.
• 12. Khalili H, Oterkus S, Barltrop N, Bharadwaj U. Different Bayesian methods for updating the fatigue crack size distribution in a tubular joint. Journal of Offshore Mechanics and Arctic Engineering 2020, https://doi.org/10.1115/1.4048155.
• 13. Kolmogorov A. Foundations of the theory of probability: Second English Edition.
• 14. Kowaka M, Tsuge H. Introduction to life prediction of industrial plant materials: Application of the extreme value statistical method for corrosion analysis. Allerton Press 1994; 26(10): 559-559, https://doi.org/10.1002/mawe.19950261013.
• 15. Kulicki J M, Prucz Z, Sorgenfrei D F et al. Guidelines for evaluating corrosion effects in existing steel bridges. 1990.
• 16. Lee J Y, Sim C, Detweiler C, Barnes B. Computer-Vision Based UAV Inspection for Steel Bridge Connections. Structural Health Monitoring 2019 2019, https://doi.org/10.12783/shm2019/32473.
• 17. Mi J, Li Y-F, Beer M et al. Importance measure of probabilistic common cause failures under system hybrid uncertainty based on bayesian network. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(1): 112, https://doi.org/10.17531/ein.2020.1.13.
• 18. National Association of Corrosion. Internal corrosion direct assessment methodology for liquid petroleum pipelines. NACE International: 2008.
• 19. Nikhil V P, Wiston B R, Ashok M. Flaw detection and monitoring over corroded surface through ultrasonic C-scan imaging. Engineering Research Express 2020; 2(1): 015010, https://doi.org/10.1088/2631-8695/ab618d.
• 20. Pham N H, La H M, Ha Q P et al. Visual and 3D mapping for steel bridge inspection using a climbing robot. ISARC 2016-33rd International Symposium on Automation and Robotics in Construction, 2016, https://doi.org/10.22260/ISARC2016/0018.
• 21. Reiss R-D, Thomas M. Statistical analysis of extreme values: from insurance, finance, hydrology and other fields. Springer: 1997, https://doi.org/10.1007/978-3-0348-6336-0.
• 22. Scarf P A, Laycock P J. Applications of extreme value theory in corrosion engineering. Journal of Research of the National Institute of Standards and Technology 1994; 99: 313-313, https://doi.org/10.6028/jres.099.028.
• 23. Schneider C. Extreme Value Analysis and Corrosion Mapping Data. Paper presented at 4th European-American Workshop on Reliability of NDE, 2009; 24: 26.
• 24. Selech J, Andrzejczak K. An aggregate criterion for selecting a distribution for times to failure of components of rail vehicles. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22 (1): 102–111, http://dx.doi.org/10.17531/ein.2020.1.12.
• 25. Shibata T. Corrosion probability and statistical evaluation of corrosion data. Uhlig's Corrosion Handbook, third Edition, 2011; 21: 367, https://doi.org/10.1002/9780470872864.ch27.
• 26. Si X-S, Wang W, Hu C-H, Zhou D-H. Remaining useful life estimation-a review on the statistical data driven approaches. European Journal of Operational Research 2011; 213(1): 1-14, https://doi.org/10.1016/j.ejor.2010.11.018.
• 27. Sobaszek Ł, Gola A, Świć A. Time-based machine failure prediction in multi-machine manufacturing systems. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22 (1): 52–62 http://dx.doi.org/10.17531/ein.2020.1.7.
• 28. Stone M. Wall thickness distributions for steels in corrosive environments and determination of suitable statistical analysis methods. Proc. 4th Eur.-Amer. Workshop Rel. NDE, 2009: 1-23.
• 29. TWI Limited. Guidelines for use of statistics for analysis of sample inspection of corrosion. 2002.
• 30. Villa T F, Gonzalez F, Miljievic B et al. An overview of small unmanned aerial vehicles for air quality measurements: Present applications and future prospectives. Sensors 2016; 16(7): 1072, https://doi.org/10.3390/s16071072.
• 31. Wang R, Kawamura Y. Development of climbing robot for steel bridge inspection. Industrial Robot: An International Journal 2016; 43(4): 429-447, https://doi.org/10.1108/IR-09-2015-0186.
• 32. Wang R, Kawamura Y. An automated sensing system for steel bridge inspection using GMR sensor array and magnetic wheels of climbing robot. Journal of Sensors 2016, https://doi.org/10.1155/2016/8121678.
• 33. Washer G, Nasrollahi M, Applebury C et al. Proposed guideline for reliability-based bridge inspection practices. 2014, https://doi.org/10.17226/22277.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).