A Systematic Review of AI-Based PHM Frameworks for Ship Systems and Equipment
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Published
Oct 18, 2026
Luis Fernando Mendoza Cardona
Joan Suarez
Edwin Puertas Edwin Paipa-Sanabria Juan C Martinez-Santos
https://orcid.org/0009-0007-7874-8261
Edwin Puertas Edwin Paipa-Sanabria Juan C Martinez-Santos
Abstract
Maritime operations require high levels of availability and safety under changing cargo, operational, and environmental conditions. Therefore, anticipating degradation and failures in critical ship systems is an important operational requirement.
Although predictive maintenance has advanced rapidly through data-driven approaches and artificial intelligence (AI), the available literature remains fragmented and does not consistently establish the relationships among the ship systems analyzed, the methodological approaches employed, and the conditions required for operational implementation.
This article presents a systematic literature review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to examine the development of AI-based predictive maintenance for ship systems from a Prognostics and Health Management (PHM) perspective. Scopus and Web of Science were searched, and predefined screening, eligibility, inclusion, and exclusion criteria were applied.
The findings show that research is strongly concentrated on propulsion systems, classified under Ship Work Breakdown Structure (SWBS) Group 200, and electric-plant systems, classified under SWBS Group 300, with most studies focusing on detection and diagnosis. Prognostic and prescriptive applications remain comparatively less developed. Methodologically, the literature is dominated by machine-learning and deep-learning approaches using operational and condition-monitoring variables associated with different operating regimes.
Overall, maritime PHM exhibits uneven levels of functional and implementation maturity. Important challenges remain in model comparability, independent validation, and sustained onboard deployment, while opportunities persist for extending PHM to underrepresented ship systems and validating these approaches in real-world fleet operations
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Keywords
Artificial intelligence, Prognostics and Health Management, Predictive maintenance, Ship machinery, Ship systems
References
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Kumar, A., & Flores-Cerrillo, J. (2024). Machine learning in python for process and equipment condition monitoring, and predictive maintenance: From data to process insights. MLforPSE. Retrieved from https://play.google.com/store/books/details?id=7JXtEAAAQBAJ (Machine Learning for Process Industry, Book 3)
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Lim, S., Oh, J., & Park, J. (2025). Maintenance time prediction for predictive maintenance of ship engines. Applied Sciences, 15(9), 4764. Retrieved from https://www.mdpi.com/2076-3417/15/9/4764 doi: 10.3390/app15094764
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Mandala, V., Senthilnathan, T., Suganyadevi, S., Gobhinath, S., Selvaraj, D., & Dhanapa, R. (2023). An optimized back propagation neural network for automated evaluation of health condition using sensor data. Measurement: Sensors, 29, 100846. doi: 10.1016/j.measen.2023.100846
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