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

Fault tree analysis and artificial neural network modelling for establishing a predictive ship machinery maintenance methodology

A dynamic fault tree model for a ship main engine is developed in order to analyse and identify critical systems/components of the main engine. The identified most critical systems are then used as input in an artificial neural network. An autoregressive dynamic time series neural network modelling approach is examined in a container ship case study, in order to monitor and predict future values of selected physical parameters of the most critical ship machinery equipment obtained from the fault tree analysis. The case study results of the combination of the fault tree analysis and artificial neural network model demonstrated promising prospects for establishing a dense methodology for ship machinery predictive maintenance by successfully identifying critical ship machinery systems and accurately forecasting the performance of machinery parameters

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

Dynamic risk and reliability assessment for ship machinery decision making

The proposed research, through INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project tackles the issue of predictive ship machinery inspection by enhancing reliability and safety, avoiding accidents, and protecting the environment. This paper presents the development of Machinery Risk/Reliability Analysis (MRA). The innovation of this model is the consideration and assessment of components’risk of failure and reliability degradation by utilizing raw input data. MRA takes into account the system’s dynamic state change, concerning failure rate variation over time. The presented methodology involves the generation of Markov Chains integrated with the advantages of Bayesian Belief Networks (BBNs). INCASS project developed a measurement campaign, where real time sensor data is recorded onboard a tanker, bulk carrier and container ship. The gathered data is utilized for MRA DSS tool validation and testing. Following research involves components and systems interdependencies and feed the continuous dynamic probabilistic condition monitoring algorithm

Ship machinery and equipment inspection tool development for risk control and decision making

Concerning the successful business competence, strategic planning should be enhanced considering assets availability by involving maintenance and reliability operational aspects. The INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project aims to tackle the issue of ship inspection, identification of high-risk ships, providing access to information related to ship surveys and incorporate enhanced and harmonized cooperation of maritime stakeholders in order to avoid ship accidents, promote maritime safety and protect the environment. The current research consists of machinery and equipment specifications and stakeholders’ data requirements. Focusing on the methodology perspective, a Machinery Risk Analysis (MRA) model is introduced. All progress and methodology development takes place in Java programm ing language. Overall, the outcomes of this study demonstrate the reliability performance of marine machinery components. Future development include dynamic failure rate variation through time, probabilistic model’s sensitivity analysis and components’ and systems’ interdependencies in a user-friendly Graphical User Interface (GUI) design

Predicting ship machinery system condition through analytical reliability tools and artificial neural networks

Inadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting 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 and endangering lives on-board. However, machinery condition and fault developing trends are often highly nonlinear and time-series dependent. This paper addresses the above by developing a neural network methodology alongside reliability analysis tools. Critical ship main engine systems/components are used as input in a dynamic time series neural network, in order to monitor and predict future values of physical parameters related to ship critical systems. The critical main engine systems/components and their relevant parameters to be monitored are identified though the combination of Fault Tree Analysis (FTA) and Failure Mode and Effects Analysis (FMEA). A case study of a Panamax size container ship is presented in which Artificial Neural Networks (ANN) are used to predict the upcoming future values of all main engine cylinders exhaust gas temperatures, identified as critical parameters through the FTA and FMEA tools. The suggested methodology alongside the case study results for the main engine system demonstrate that ANN predictions were accurate and can provide the platform for predictive maintenance strategies that can assist decision makers in selecting the correct maintenance actions for critical ship machinery. The case study results for the main engine system demonstrated that the ANN predictions were accurate based on past observations. The proposed methodology successfully presented a systematic approach for identifying critical systems/components through FTA/FMEA and monitoring their physical parameters through the ANN model

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme