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Purpose The purpose of this paper is to review maintenance practices, tools and parameters for marine mechanical systems that can be classified as plant, machinery and equipment (PME). It provides an insight for the maintenance crew on which maintenance parameters and practices are critical for a given PME systems. Design/methodology/approach The review paper characterizes the various maintenance parameters and maintenance practices used onshore and offshore for PME and identifies the possible gaps. Findings A variety of maintenance techniques are being used in the marine industry such as corrective maintenance, preventive maintenance and condition-based maintenance. As marine vehicles (MV) get older, the most important maintenance parameters become maintenance costs, reliability and safety. Maintenance models that have been developed in line with marine mechanical systems have been validated using a single system, whose outcome could be different if another PME system is used for validation. Research limitations/implications There is a limited literature on MV maintenance parameters and maintenance characterization regarding mechanical systems. The maintenance practices or strategies of marine mechanical systems should be based on maintenance parameters that suit the marine industry for a given PME. Originality/value Based on the available literature, the paper provides a variety of maintenance framework, parameters and practices for marine mechanical systems. The paper further gives an insight on what maintenance parameters, strategies and platforms are given preference in the shipping industry.

This article proposes a stochastic technique for determining the optimal maintenance policy for marine mechanical systems. The optimal maintenance policy output includes the average maintenance cost rate, maintenance interval and the performance thresholds for the three marine mechanical system classifications. The purpose of this study is to optimize maintenance, maintenance interval and performance thresholds based on maintenance and reliability data of the marine mechanical systems. Performance threshold and maintenance interval are used as the decision variables to determine the optimal maintenance policy. A stochastic model based on probability analysis is developed to trigger the maintenance action for mechanical systems. The model is later coded in MATLAB. Maintenance and failure data for a marine vehicle were statistically fitted using ReliaSoft, from which a three-parameter Weibull distribution best fitted all the mechanical system classifications. Model inputs were based on both the maintenance data and expert knowledge of the maintenance crew. Based on a 20-year marine vehicle life span, the optimal maintenance costs for plant and machinery are relatively the same. The model predicted annual total maintenance cost of US$183,029.24 is 11.11% more than the maintenance cost derived from experts’ threshold of US$164,726. Marine vehicle machinery presents a higher maintenance interval of 3.23 years compared to 2.92 years for marine vehicle plants. It was observed that for the performance thresholds greater than 84.54%, there is an insignificant difference between the plant and machinery maintenance costs. Sensitivity analysis results suggest there is little justification that changing maintenance costs will have an impact on the performance threshold [Formula: see text] and maintenance interval [Formula: see text]. A maintenance interval of 3 years results in a lower total annual maintenance cost deviation of 2.66% from the optimal total annual maintenance cost.

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There are many ways to model and to analyze discrete event systems. In general these systems lead to a non-linear characteristic equation description in linear algebra. This paper presents an analytical method for solving the characteristic equation of higher order, which arise when solving ordinary differential equations of motion of rigid body systems with 2 ≤ p° ≤ 10 degrees of freedom. The objective of this work was to express the characteristic equation in the form of product quadratic polynomial, from which the modal components could be found. To validate the model, the modal parameters extraction technique – Ibrahim Time Domain (ITD) – was used to extract modal parameters from artificial data developed in MATLAB environment. The extracted modal components were compared to those obtained from the analytical model.