3 resultados para Maintenance strategies

em Aston University Research Archive


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Proper maintenance of plant items is crucial for the safe and profitable operation of process plants, The relevant maintenance policies fall into the following four categories: (i) preventivejopportunistic/breakdown replacement policies, (ii) inspection/inspection-repair-replacernent policies, (iii) restorative maintenance policies, and (iv) condition based maintenance policies, For correlating failure times of component equipnent and complete systems, the Weibull failure distribution has been used, A new powerful method, SEQLIM, has been proposed for the estimation of the Weibull parameters; particularly, when maintenance records contain very few failures and many successful operation times. When a system consists of a number of replaceable, ageing components, an opporturistic replacernent policy has been found to be cost-effective, A simple opportunistic rrodel has been developed. Inspection models with various objective functions have been investigated, It was found that, on the assumption of a negative exponential failure distribution, all models converge to the same optimal inspection interval; provided the safety components are very reliable and the demand rate is low, When deterioration becomes a contributory factor to same failures, periodic inspections, calculated from above models, are too frequent, A case of safety trip systems has been studied, A highly effective restorative maintenance policy can be developed if the performance of the equipment under this category can be related to some predictive modelling. A novel fouling model has been proposed to determine cleaning strategies of condensers, Condition-based maintenance policies have been investigated. A simple gauge has been designed for condition monitoring of relief valve springs. A typical case of an exothermic inert gas generation plant has been studied, to demonstrate how various policies can be applied to devise overall maintenance actions.

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The existing method of pipeline monitoring, which requires an entire pipeline to be inspected periodically, wastes time and is expensive. A risk-based model that reduces the amount of time spent on inspection has been developed. This model not only reduces the cost of maintaining petroleum pipelines, but also suggests an efficient design and operation philosophy, construction method and logical insurance plans.The risk-based model uses analytic hierarchy process, a multiple attribute decision-making technique, to identify factors that influence failure on specific segments and analyze their effects by determining the probabilities of risk factors. The severity of failure is determined through consequence analysis, which establishes the effect of a failure in terms of cost caused by each risk factor and determines the cumulative effect of failure through probability analysis.

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Knowledge maintenance is a major challenge for both knowledge management and the Semantic Web. Operating over the Semantic Web, there will be a network of collaborating agents, each with their own ontologies or knowledge bases. Change in the knowledge state of one agent may need to be propagated across a number of agents and their associated ontologies. The challenge is to decide how to propagate a change of knowledge state. The effects of a change in knowledge state cannot be known in advance, and so an agent cannot know who should be informed unless it adopts a simple ‘tell everyone – everything’ strategy. This situation is highly reminiscent of the classic Frame Problem in AI. We argue that for agent-based technologies to succeed, far greater attention must be given to creating an appropriate model for knowledge update. In a closed system, simple strategies are possible (e.g. ‘sleeping dog’ or ‘cheap test’ or even complete checking). However, in an open system where cause and effect are unpredictable, a coherent cost-benefit based model of agent interaction is essential. Otherwise, the effectiveness of every act of knowledge update/maintenance is brought into question.