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Thekeys to this analysis are a set of mathematical models of the effects of maintenanceactivities and expert judgement about which of the models is most suitable under aparticular set of circumstances. These features are incorporated in the IMOSsoftware as a 'model base1 module, consisting of a set of routines for eachmathematical model, and a 'rule base' module which selects the most appropriatemodels by recognising characteristic patterns in the historical data for each item ofequipment. There are no previous attempts in the maintenance literature to formulatesuch a list of rules to guide model selection.The study and modelling of industrial maintenance is reviewed, as is relevant work onthe support of management decision making and the features and evolution of DSS isalso discussed. The need for and benefits of a system such as IMOS are describedand the suitability of the intelligent decision support system approach is discussed.The mathematical models, the selection rules, and optimisation criteria andtechniques are detailed, and the development of the software, written in C for anIBM compatible PC, is described. The research was conducted in collaboration withtwo major oil exploration and production companies and data from several North Seaoil-production platforms are analysed and discussed. Finally, achievements andshortcomings of the system are discussed and some suggestions for further researchoutlined.","abstract_html":"This thesis describes the background to and development of a computer-baseddecision support system (DSS) known as IMOS, the intelligent maintenanceoptimisation system. The aim of the system is to help industrial maintenanceengineers improve the planned preventive maintenance policies applied to large andcomplex technical systems. IMOS attempts to achieve this by providing someautomated analysis of the huge amounts of maintenance history information which isaccumulating in the computerised data bases of many large industrial companies. Thekeys to this analysis are a set of mathematical models of the effects of maintenanceactivities and expert judgement about which of the models is most suitable under aparticular set of circumstances. These features are incorporated in the IMOSsoftware as a 'model base1 module, consisting of a set of routines for eachmathematical model, and a 'rule base' module which selects the most appropriatemodels by recognising characteristic patterns in the historical data for each item ofequipment. There are no previous attempts in the maintenance literature to formulatesuch a list of rules to guide model selection.The study and modelling of industrial maintenance is reviewed, as is relevant work onthe support of management decision making and the features and evolution of DSS isalso discussed. The need for and benefits of a system such as IMOS are describedand the suitability of the intelligent decision support system approach is discussed.The mathematical models, the selection rules, and optimisation criteria andtechniques are detailed, and the development of the software, written in C for anIBM compatible PC, is described. The research was conducted in collaboration withtwo major oil exploration and production companies and data from several North Seaoil-production platforms are analysed and discussed. 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There are no previous attempts in the maintenance literature to formulatesuch a list of rules to guide model selection.The study and modelling of industrial maintenance is reviewed, as is relevant work onthe support of management decision making and the features and evolution of DSS isalso discussed. The need for and benefits of a system such as IMOS are describedand the suitability of the intelligent decision support system approach is discussed.The mathematical models, the selection rules, and optimisation criteria andtechniques are detailed, and the development of the software, written in C for anIBM compatible PC, is described. The research was conducted in collaboration withtwo major oil exploration and production companies and data from several North Seaoil-production platforms are analysed and discussed. 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