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Structural Monitoring System for proactive detection of corrosion and coating failure.

Latif, J., Khan, Z. A. and Stokes, K., 2020. Structural Monitoring System for proactive detection of corrosion and coating failure. Sensors and Actuators A: Physical, 301 (January), 111693.

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SHM_revised_accepted version_221019.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1016/j.sna.2019.111693


The performance and availability of high priority structures can be greatly affected by corrosion damage. The application of protective coatings, frequent inspections and scheduled based maintenance activities result in huge direct and indirect financial loss to organisations. The expeditious detection of coating failure and corrosion damage can result in precise and cost-effective condition-based maintenance. Coating failure and corrosion phenomena are driven by complex multi-disciplinary parameters according to extensive research findings in the literature. State-of-the-art prognostic models proposed in recent years incorporate complex multi-disciplinary parameters, therefore a real-time prognostic monitoring system must acquire these complex parameters to allow accurate prediction. The work reported here covers the development of a real-time monitoring system using micro-sensors and includes the validation of the system through accelerated corrosion and coating failure testing. The system contains a remote terminal unit that includes a linear polarisation method for corrosion detection under the coating and a micro-strain gauge method for monitoring stress behaviour over the coating. The software at base station includes a graphical user interface and database to store parameters for further processing and failure prediction. The real-time monitoring system can be applied to remote, stationary and mobile assets to monitor the mechanical and chemical changes within coating-substrate systems.

Item Type:Article
Uncontrolled Keywords:Corrosion ; Condition monitoring ; Coating failures ; Micro-sensors ; Remote sensing ; Failure prediction
Group:Faculty of Science & Technology
ID Code:32990
Deposited By: Symplectic RT2
Deposited On:01 Nov 2019 14:38
Last Modified:14 Mar 2022 14:18


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