Use this URL to cite or link to this record in EThOS:
Title: Development of an innovative business-oriented probability-based maintenance (BOPM) methodology for ship machinery systems
Author: Taheri, Atabak
ISNI:       0000 0004 7425 3190
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
Throughout the maritime industry, there has been relatively high number of shipping-related incidents. Therefore, numerous international, local and Classification Society based legislations have been developed in order to regulate shipping and reduce accidents. These policies not only dictate ship design methodologies but also inspection and maintenance activities of vessels. These policies on inspection and maintenance have generally increased the cost of shipping in the world. As a result, there has been substantial research on the risk and cost aspects of maintenance in the maritime industry. However, no research has put emphasised risk and technical aspects of maintenance with the business and cost related aspects of maintenance in one unified platform. Therefore, this PhD has developed an overall methodology in order to combine cost and business oriented aspects of a shipping company with their risk and technical aspects. This methodology is called Business Oriented Probability-based Maintenance (BOPM). In this methodology, company business aspects and Maintenance Performance Indicators (MPIs) have been used to modify and personalise maintenance and repair cost values, risk factors (human risk, environmental risk, cost of failure and loss of operation), and component/sub-system performance reading limits. Performance limits from OEM reports modified by company specific inputs are then used to determine probabilistic performance values based on the monitored live values received from vessels. Subsequently, these probabilistic values are placed in a Probabilistic Analysis Unit (PAU) within the BOPM platform to predict the future performance values for each component/sub-system within the system. This PAU model uses an innovative Dynamic Bayesian Network (DBN) with first order Markov Chains to predict the future probabilistic pattern of each system monitored from the vessel. Afterward, net cost analysis is performed using cost values modified by company MPIs inside utility and decision nodes added to the DBN model in order to provide cost-based decisions on the performance of each component and schedule specific maintenance or repair dates if required. In the other section of the BOPM risk values are combined with their probability of failure using a Fuzzy Set Theory (FST) in order to determine a final relevant risk value for each component/sub-system. Finally, obtained risk values are combined with decisions from the cost-based DBN Decision Analysis Unit (DAU) to prioritise tasks that are intervening with each other. The overall methodology was approved and validated by both industrial experts and using results and conclusions made from the INCASS EU FP7 project that I was also involved in. Three similar systems from three vessels have been used as the case studies in order to analyse the effectiveness of the BOPM platform and validate its results. These vessels are two chemical tanker sister ships and one general cargo vessel. Three similar system types from each vessel have been used namely the Lub-oil system, Fuel-oil system and Turbocharger. Having two sister ships operating in different environments has also created the possibility of evaluating the effects of environmenton performance of each system. Using the overall BOPM analysis platform, relative probabilistic performance and availability of all the sub-systems/components within the main observed systems were predicted for four future time slices. This was then compared with actual observed performance value and it was noted that the overall methodology has an accuracy of 97.8%. Subsequently, using the decision-making part of the methodology, future maintenance tasks were recommended. This was then compared with the maintenance logs of all three vessels and it was observed that they were not simply matching but also exceeding their recommendations and saving the company an extra $467. Finally, the results obtained also proved that the overall results and scope of the thesis have helped to meet and exceed the overall goals and targets of the company. Keywords: Business-oriented Probability-based Maintenance (BOPM), Dynamic Bayesian Network (DBN), Markov Chain, Net cost analysis, Decision-making, Risk factors, Maintenance Performance Indicators (MPIs), Technical and business aspects.
Supervisor: Iraklis, Lazakis Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral