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

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

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

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

Dynamic predictive reliability assessment of ship systems

Recent research shows that maritime industry has adopted innovative and sophisticated inspection and maintenance practices. A flexible framework, applicable on complex machinery, is introduced towards ship maintenance. A holistic inspection and maintenance notion is implemented, introducing different strategies, methodologies, and tools, suitably selected, for each required ship system. The proposed framework enables predictive reliability assessment of ship machinery, while scheduling maintenance actions by enhancing safety and systems' availability. This paper presents the Probabilistic Machinery Reliability Assessment (PMRA) strategy, which achieves predictive reliability assessment and evaluation of different complex ship systems. The assessment takes place on system, subsystem and component level, while allowing data fusion of different data types from various input sources. In this respect, the combination of data mining method (k-means), manufacturers' alarm levels, dynamic state modelling (Markov Chains), probabilistic predictive reliability assessment (Dynamic Bayesian Belief Networks) and qualitative decision making (Failure Modes and Effects Analysis) is suggested. PMRA has been clearly demonstrated in a case study on selected ship machinery. The results identify the most unreliability systems, subsystems and components, while advising practical maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Advanced ship systems condition monitoring for enhanced inspection, maintenance and decision making in ship operations

Structural and machinery failures in the day-to-day ship operations may lead to major accidents, endangering crew and passengers onboard, posing a threat to the environment, damaging the ship itself and having a great impact in terms of business losses. In this respect, this paper presents the INCASS (Inspection Capabilities for Enhanced Ship Safety) project which aims bringing an innovative solution to the ship inspection regime through the introduction of enhanced inspection of ship structures, 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 input to the Decision Support System (DSS). Enhanced inspection of ships will also include 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. Moreover, condition based inspection tools and methodologies, reliability and criticality based maintenance are introduced. An enhanced central database handles ship structures and machinery data. The data is available to ship operators and are utilized by the DSS for ship structures and machinery for continuous monitoring and risk analysis of ship operations. The development and implementation of the INCASS system is shown in the case of a machinery system of a tanker ship. In this way the validation and testing of the INCASS framework will be achieved in realistic operational conditions

Probabilistic risk assessment of condition monitoring of marine diesel engines

This paper scopes to provide a suggestion on the maintenance outline as perceived from the evaluation of an extended literature review on marine engineering systems and applications sourced from research and industrial studies. The present research contributes in the creation and initial implementation of a probabilistic multi-component prognostic Condition Monitoring model for ship machinery and equipment maintenance scheduling. Systems involved include engine internal and external components, starting, cooling, and lubrication and control monitoring systems. The overall reliability performance of these sub-systems and the entire Main Engine’s is suggested. Moreover, this paper will present the components and failure types’ layout arrangement of engine internal and external sub-systems as well as the overall reliability performance of the sub-system

Novel inspection and maintenance platform for enhanced ship safety

The research work presented in this paper addresses issues related to ship inspection and maintenance through the development of a novel platform as part of the INCASS (Inspection Capabilities for Enhanced Ship Safety) project. INCASS addresses ship safety including structural and machinery monitoring, data gathering, reliability analysis and decision support. As part of the above, a case study on the machinery monitoring will be presented in this paper. The analysis involves ship’s main engine, lubrication and starting sub-systems and various components and failure modes. The case study presents the working state reliability performance on system, sub-system and components levels