A Risk-based Method for Selecting Ship Machinery Maintenance Strategies

¹ Nikola Vaptsarov Naval Academy, Varna, Bulgaria
² TEAM4Excellence Research Center, Constanta, Romania
³ Constanta Maritime University, Constanța, Romania

DOI: 10.12716/1001.20.03.05

Previous paper - Contents - Next paper

ABSTRACT: Ship machinery maintenance must balance safety, reliability, availability, regulatory compliance and cost under operational constraints and frequently incomplete data. Conventional approaches based on fixed maintenance intervals, corrective intervention or isolated condition monitoring remain necessary, but they do not provide a consistent basis for selecting among alternative maintenance strategies when failure likelihood, consequence severity, equipment condition, uncertainty, feasibility and cost interact. This paper proposes an integrated risk-based method for selecting maintenance strategies at machinery, component and failure-mode level. Failure-mode analysis, reliability and conditional probability assessment, condition evidence, consequence assessment, risk classification and uncertainty treatment provide structured inputs to a Maintenance Decision Engine. Candidate strategies - including corrective, time-based preventive, condition-based, predictive, risk-based, opportunity and deferred maintenance -are first screened against statutory, classification, manufacturer, safety and technical constraints. Only admissible alternatives proceed to multi-criteria ranking using the Technique for Order Preference by Similarity to Ideal Solution. The ranking considers expected risk reduction, technical suitability, reliability and availability contribution, implementation feasibility, expected total cost, and evidence confidence. Ranking margins and sensitivity analysis are used to assess robustness, while post-ranking engineering review confirms, modifies or overrides the numerical recommendation according to condition, risk urgency, redundancy and operational feasibility. The selected strategy is then converted into a traceable maintenance action and recorded through the planned or computerised maintenance management system. The principal contribution is the integration of established engineering methods within a constrained, transparent and auditable maintenance decision process.

KEYWORDS: Risk Assessment, Marine Engineering, Operational Efficiency, Reliability, Methods and Algorithms, Ship Maintenance, Decision Support Systems, Maintenance Strategies

REFERENCES

ABS, 2018a. Guidance Notes on Failure Mode and Effects Analysis (FMEA) for Classification. Houston, TX: American Bureau of Shipping.

ABS, 2018b. Guidance Notes on Reliability-Centered Maintenance. Houston, TX: American Bureau of Shipping.

Asuquo, M.P., Wang, J., Zhang, L. and Phylip-Jones, G., 2019. Application of a multiple attribute group decision making (MAGDM) model for selecting appropriate maintenance strategy for marine and offshore machinery operations. Ocean Engineering, 179, pp.246–260. doi:10.1016/j.oceaneng.2019.02.065. - doi:10.1016/j.oceaneng.2019.02.065

Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M. and Ignatius, J., 2012. A state-of-the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), pp.13051–13069. doi:10.1016/j.eswa.2012.05.056. - doi:10.1016/j.eswa.2012.05.056

Cheliotis, M., Lazakis, I. and Theotokatos, G., 2020. Machine learning and data-driven fault detection for ship systems operations. Ocean Engineering, 216, 107968. doi:10.1016/j.oceaneng.2020.107968. - doi:10.1016/j.oceaneng.2020.107968

Daya, A.A. and Lazakis, I., 2024. Systems reliability and data driven analysis for marine machinery maintenance planning and decision making. Machines, 12(5), 294. doi:10.3390/machines12050294. - doi:10.3390/machines12050294

Emovon, I., 2016. Multi-criteria Decision Making Support Tools for Maintenance of Marine Machinery Systems. PhD thesis. Newcastle University. Available through Newcastle University eTheses, handle 10443/3280.

Hwang, C.-L. and Yoon, K., 1981. Multiple Attribute Decision Making: Methods and Applications: A State-of-the-Art Survey. Lecture Notes in Economics and Mathematical Systems, Vol. 186. Berlin and Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-48318-9. - doi:10.1007/978-3-642-48318-9

IACS, 2018. Recommendation No. 74: A Guide to Managing Maintenance in Accordance with the Requirements of the ISM Code. Rev. 2, August 2018. London: International Association of Classification Societies.

IEC, 2019. IEC 31010:2019 Risk Management — Risk Assessment Techniques. Geneva: International Electrotechnical Commission.

IMO, 2018. International Safety Management Code (ISM Code) and Guidelines on Implementation of the ISM Code. London: International Maritime Organization.

ISO, 2003. ISO 13374-1:2003 Condition Monitoring and Diagnostics of Machines — Data Processing, Communication and Presentation — Part 1: General Guidelines. Geneva: International Organization for Standardization.

ISO, 2018a. ISO 17359:2018 Condition Monitoring and Diagnostics of Machines — General Guidelines. Geneva: International Organization for Standardization.

ISO, 2018b. ISO 31000:2018 Risk Management — Guidelines. Geneva: International Organization for Standardization.

Jardine, A.K.S., Lin, D. and Banjevic, D., 2006. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), pp.1483–1510. doi:10.1016/j.ymssp.2005.09.012. - doi:10.1016/j.ymssp.2005.09.012

Karatuğ, Ç., Arslanoğlu, Y. and Guedes Soares, C., 2022. Determination of a maintenance strategy for machinery systems of autonomous ships. Ocean Engineering, 266, 113013. doi:10.1016/j.oceaneng.2022.113013. - doi:10.1016/j.oceaneng.2022.113013

Khan, F.I., Sadiq, R. and Haddara, M.M., 2004. Risk-based inspection and maintenance (RBIM): Multi-attribute decision-making with aggregative risk analysis. Process Safety and Environmental Protection, 82(6), pp.398–411. doi:10.1205/psep.82.6.398.53209. - doi:10.1205/psep.82.6.398.53209

Lazakis, I. and Ölçer, A.I., 2016. Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision-making environment. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 230(2), pp.297–309. doi:10.1177/1475090215569819. - doi:10.1177/1475090215569819

Lazakis, I., Turan, O., Aksu, S. and Incecik, A., 2010. Increasing ship operational reliability through the implementation of a holistic maintenance management strategy. Ships and Offshore Structures, 5(4), pp.337–357. doi:10.1080/17445302.2010.480899. - doi:10.1080/17445302.2010.480899

Lloyd’s Register, NYK Line and MTI, 2024. Data-Driven Condition Based Maintenance. London and Tokyo: Lloyd’s Register, NYK Line and Monohakobi Technology Institute.

Moubray, J., 1997. Reliability-Centred Maintenance. 2nd ed. Oxford: Butterworth-Heinemann.

Rødseth, Ø.J., Steinebach, C. and Mo, B., 2007. The use of technical condition indices in ship maintenance planning and the monitoring of the ship’s safety condition. In: Proceedings of the International Symposium on Maritime Safety, Security and Environmental Protection, Athens, Greece, 20–21 September 2007.

Thoben, K.-D., Homburg, N. and Gerriets, A., 2008. MarLife: Development of a life cycle management system for the maritime industry. In: Ship Repair Technology Symposium, Newcastle-upon-Tyne, 1–2 September 2008, pp.98–105.

Vose, D., 2008. Risk Analysis: A Quantitative Guide. 3rd ed. Chichester: Wiley.

Zio, E., 2013. The Monte Carlo Simulation Method for System Reliability and Risk Analysis. London: Springer. - doi:10.1007/978-1-4471-4588-2

Citation note:

Acomi O., Belev B., Chervinskyi M., Acomi N.: A Risk-based Method for Selecting Ship Machinery Maintenance Strategies. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 20, No. 3, doi:10.12716/1001.20.03.05, pp. 569-583, 2026

BibTeX EndNote

Authors in other databases:

Ovidiu Acomi: ORCID iD icon

orcid.org/0000-0001-9308-1441 Scopus icon