Adoption of prescriptive analytics for naval vessels risk-based maintenance:A conceptual framework. (15th June 2023)

• Record Type: Journal Article
• Authors: Mat Esa, Mohd Adha; Muhammad, Masdi
• Is Part Of: Ocean engineering. Volume 278(2023)
• Publication Date: 2023-06-15
• Subjects: Naval maintenance -- Prescriptive analytics -- Risk-based maintenance; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2023.114409
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Prescriptive analytics is a novel maintenance approach by recommending the maintainer, the right maintenance action at the right time through prioritization of maintenance based on the risk. It assists in improving the existing preventive maintenance approach by offering a dynamic maintenance plan based on risk or Risk-based Maintenance Scheduling. To date, there is no clear definition of Naval Vessels Risk-based Maintenance as well as its framework, and it has not been applied extensively in naval vessel maintenance application which is peculiar compared to its application in plant maintenance. This paper reviews Risk-based Maintenance application in industries in an approach to develop prescriptive analytics for a comprehensive and dynamic naval vessels Risk-based Maintenance decision-making framework. It aims to establish an optimal maintenance scheduling to improve naval vessels availability and reduce maintenance costs while meeting stakeholders' expectations. Objectives and considerations are also defined for peculiarities application of naval vessels. The framework is then evaluated to improve the effectiveness of the current Preventive Maintenance practice and the inconsistency of the existing maintenance decision-making model using the expert judgement in Reliability Centered Maintenance. Highlights: Prescriptive analytics for a comprehensive and dynamic naval vessels risk-based maintenance decision making framework . Preventive Maintenance ineffectiveness and inconsistency issue of the existing maintenance decision making model by using expert judgment. A novel methodology using function-system hierarchy tree risk assessment and prescriptive analytics (predictive maintenance and machine learning).