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Title: Establishment of a novel predictive reliability assessment strategy for ship machinery
Author: Dikis, Konstantinos
ISNI:       0000 0004 6349 9275
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2017
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral