Journal article
Coating breakdown prediction for ships and inspection planning
Marine Structures, Vol.110, pp.1-23
2026
Abstract
Marine corrosion significantly reduces a ship's availability, increases costs of operation and could impact safety. Protective coatings mitigate these risks, but their effectiveness deteriorates over time. Early detection of coating breakdown is crucial to prevent costly repairs and safety concerns. While corrosion itself is well-understood, coating degradation remains under-investigated due to insufficient long-term data. This work addresses this knowledge gap by enhancing coating defect prediction and optimizing inspection planning for ships. The Power Law Non-Homogeneous Poisson Process (PL-NHPP) is utilized for modeling coating defect arrivals. Unlike prior studies, we employ a hierarchical Bayesian approach for parameter fitting, effectively addressing limitations associated with scarce real-world data. Furthermore, we optimize inspection planning by incorporating out-of-service costs and potential costs increases due to delayed repairs. The efficacy of these methods is evaluated through a comprehensive case study involving a recently commissioned fleet with limited historical data. This research contributes to the advancement of condition-based maintenance (CBM) strategies for ships by enabling more accurate prediction of coating breakdowns and optimizing inspection schedules early in the life of the fleet. This approach ultimately improves operational efficiency and reduces life-cycle costs.
Details
- Title
- Coating breakdown prediction for ships and inspection planning
- Authors
- Huy Truong-Ba (Corresponding Author) - Queensland University of TechnologyMichael E. Cholette - Queensland University of TechnologyGeoffrey Will - University of the Sunshine CoastMarc Hartmann - Thales (Australia)
- Publication details
- Marine Structures, Vol.110, pp.1-23
- Publisher
- Elsevier Ltd
- Date published
- 2026
- DOI
- 10.1016/j.marstruc.2026.104161
- ISSN
- 1873-4170
- Copyright note
- © 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
- Data Availability
- The authors do not have permission to share data.
- Grant note
- The authors would like to acknowledge the Commonwealth of Australia for their collaboration and funding.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991250588502621
- Output Type
- Journal article
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