Risk assessment for the installation and maintenance activities of a low-speed tidal energy converter

The study presented in this paper, is part of the Deep Green project, which includes the development of a power converter/device for employment in low-speed tidal currents. It mainly focuses on the initial steps to investigate the ways on how to minimize the risks during handling, operation and maintenance (O&M) activities of the full-scale device particularly in offshore operations. As a first tep, the full-scale device offshore installation and O&M tasks are considered. The overall risk analysis and decision making methodology is presented including the Hazard Identification (HAZID) approach which is complemented with a risk matrix for various consequence categories including personnel Safety (S), Environmental impact (E), Asset integrity (A) and Operation (O). In this way, all the major risks involved in the mentioned activities are identified and actions to prevent or mitigate them are presented. The results of the HAZID analysis are also demonstrated. Finally, the last section of this paper presents the discussion, conclusions and future actions for the above-mentioned activities regarding the full-scale device

An artificial neural network approach for predicting the performance of ship machinery equipment

Inadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting ship performance, having a great impact in terms of business losses by reducing ship availability and increasing downtime and moreover increasing the potential of major accidents occurring, endangering lives onboard. Efforts have being made to transform corrective/preventive maintenance techniques into predictive ones. Condition monitoring is considered as a major part of predictive maintenance. It assesses the operational health of equipment, in order to provide early warning of potential failure such that preventive maintenance action may be taken. Condition monitoring is defined as the collection and interpretation of the relevant equipment parameters for the purpose of the identification of the state of equipment changes from normal conditions and trends of the health of the equipment. The equipment condition and the fault developing trend are often highly nonlinear and time-series based. Artificial Neural Networks (ANNs) can be used due to their potential ability in nonlinear time-series trend prediction. Therefore this paper proposes the use of an autoregressive dynamic time series ANN in order to monitor and predict selected physical parameters of ship machinery equipment that contribute to the overall performance and availability, in order to predict their future values that will illustrate their performance state that will eventually lead to the correct maintenance actions and decisions

Modeling of vessel and equipment cost for the maintenance activities of an offshore tidal energy array

Tidal energy is one of the most promising sectors of energy conversion that can be extracted from renewable sources, taking into account the uninterrupted flow of tidal currents regardless of the surrounding environmental conditions. In this case, a detailed analysis of the planned and unplanned maintenance attributes has been developed in order to examine the various O&M parameters influencing the cost elements. Major features for both planned and unplanned maintenance include the identification of the transportation, labor, workshop and equipment/tools cost. The above are estimated for different operational scenarios as well as for the maintenance of a single device per day. Overall, the O&M cost per device is estimated as well as the cost per MW (gross and net) and the cost/kWhr. The results show that the overall O&M cost is not prohibitive compared to other renewable energy applications while it may vary according to the initially selected O&M scenario

The effect of increasing the thickness of the ship’s structural members on the Generalised Life Cycle Maintenance Cost (GLCMC)

In the context of the EU funded IMPROVE project, the research work of a Generalised Life Cycle Maintenance Cost (GLCMC) was initiated in order to investigate the influence of a weight oriented ship structural design on its production and operational characteristics. Following this, an increase in the structural scantlings of the ship was examined following the IACS Common Structural Rules (CSR) for double hull oil tankers. A case study for a Chemical tanker is shown considering an addition in its bottom plate thickness and three different cases of mean annual corrosion rates applied. A comparison regarding the “Gross gains”, “Gross expenses” and “Net gains” for this ship is also presented. Moreover, an evaluation of the extra cost for the additional steel weight used is shown together with the outcome on the repair-free operation of the ship for different additional plate thickness. Finally, a sensitivity analysis is carried out for the most likely case (“Case 2”) and the variation of different amount of days spent in the ship repair yard

Optimum methodology for site selection of wave energy converters

A critical review on the feasibility of extracting wave energy in the marine environment for the purpose of generating electric power has been carried out using a methodology of resource assessment to analyse the different elements involved in the site selection process and as it relates to the operation and maintenance (O&M) activities for deployment of wave energy converter (WEC) technologies. Part of the issues affecting the choice of using wave energy as an alternative source for power generation centres on the uncertainty surrounding cost of O&M for these power generation technologies. The resource assessment approach examines the different aspects of the operational process starting from the initial site selection stage, and the cost implications of various O&M practice. The objective is to establish the evidence which clearly illustrates that the selected site does not have too many environmental or technical constraints that could impinge on the development of the project. This approach will help prospective investors and developers of marine renewable technologies in planning the project implementation

Selection of the best maintenance approach in the maritime industry under fuzzy multiple attributive group decision-making environment

Many maintenance approaches have been developed and applied successfully in a variety of sectors such as aviation and nuclear industries over the years. Some of those have also been employed in the maritime industry such as condition based maintenance; however, choosing the best maintenance approach has always been a big challenge due to the involvement of many attributes and alternatives which can also be associated with multiple experts and vague information. In order to accommodate these aspects, and as part of an overall novel Reliability and Criticality Based Maintenance strategy, an existing fuzzy multiple attributive group decision-making technique is employed in this study, which is further enhanced with the use of Analytical Hierarchy Process to obtain a better weighting of the maintenance attributes used. The fuzzy multiple attributive group decision-making technique has three distinctive stages, namely rating, aggregation and selection in which multiple experts’ subjective judgments are processed and aggregated to be able to arrive at a ranking for a finite number of maintenance options. To demonstrate the applicability in a real-life industrial context, the technique is exemplified by selecting the best maintenance approach for shipboard equipment such as the diesel generator system of a vessel. The results denote that preventive maintenance is the best approach closely followed by predictive maintenance, thus steering away from the ship corrective maintenance framework and increasing overall ship system reliability and availability

Developing a risk analysis and decision making strategy for an offshore wind farm

The renewables sector and particularly offshore wind energy is a fast developing industry over the last few years. Especially activities related to the Installation,Operation and Maintenance (O&M) of offshore wind turbines becomes a challenging task with inherent risks. This paper assesses the risks related to the above stages of a wind farm lifecycle using the FMECA (Failure Mode, Effects and Criticality Analysis) and HAZID (Hazard Identification) methods. The full-scale offshore installation and O&M tasks are considered together with the wind turbine main components. An integrated risk analysis methodology is presented addressing personnel Safety (S), Environmental impact (E), Asset integrity (A) and Operation (O). The above is supplemented by a cost analysis with the aid of BBN(Bayesian Belief Networks) method in order to assist the decision making process related to installation and O&M tasks. All major risks and critical wind turbinecomponents are identified as well as measures are suggested in order to prevent or mitigate them. Moreover, a thorough inspection and maintenance plan can be elaborated for the mentioned activities

Maintenance strategies of offshore structures

The operational environment of the day-to-day industrial applications has more complex and pretentious structure, while their business effectiveness and efficiency is influenced by factors such as time, financial constraints, technology, innovation, quality, reliability, and information management. Maintenance costs consist a large part of asset management costs and a reduction in these expenditures can significantly improve business’s savings and entire operational performance

Ship machinery condition monitoring using vibration data through supervised learning

This paper aims to present an integrated methodology for the monitoring of marine machinery using vibration data. Monitoring of machinery is a crucial aspect of maintenance optimisation that is required for the vessel operation to remain sustainable and profitable. The proposed methodology will train models using pre-classified (healthy/faulty) data and then classify new data points using the models developed. For this, vibration points are first acquired, appropriately processed and stored in a database. Specific features are then extracted from the data and stored. These data are then used to train supervised models pertinent to specific machinery components. Finally, new data are compared against the models developed in order to evaluate their condition. The above will provide a flexible but robust framework for the early detection of emerging machinery faults. This will lead to minimisation of ship downtime and increase of the ship’s operability and income through operational enhancement

Condition monitoring for enhanced inspection, maintenance and decision making in ship operations

This paper presents the INCASS (Inspection Capabilities for Enhanced Ship Safety) project which brings innovative solutions to the ship inspection regime by integrating robotic-automated platforms for on-line or on-demand ship inspection activities and selecting the software and hardware tools that can implement or facilitate specific inspection tasks, to provide in- put to the Decision Support System (DSS). Enhanced inspection of ships includes ship structures and machinery monitoring with real time information using ‘intelligent’ sensors and incorporating structural and machinery risk analysis, using in-house structural/hydrodynamics and machinery computational tools. Condition based inspection tools and methodologies, reliability and criticality based maintenance are introduced. An enhanced central database handles ship structures and machinery data. The development and implementation of the INCASS system is shown in the case of ship machinery systems. In this way the validation and testing of the INCASS framework will be achieved in realistic operational conditions