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Title: The development of a predictive model for monitoring the condition of a hot strip mill
Author: Goode, K. B.
Awarding Body: University of Wales Swansea
Current Institution: Swansea University
Date of Award: 2000
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In the highly competitive steel industry, British Steel Strip Products (BSSP) has to continually focus on increased performance, product quality and efficiency, in order to maintain its market share and keep its customers. The Hot Strip Mill (HSM) has traditionally been an area of some concern to BSSP, due to unscheduled mill breakdowns causing a loss of production time and an associated reduction in product quality. BSSP have set-up condition monitoring programmes to tackle some of these problems and are currently investing a great deal of money and resources to build on these initial successes. In this thesis a review of current condition monitoring activities is over viewed together with those practised at BSSP. A prediction model is developed that is intended to help improve machine life estimation and complement BSSP's main condition monitoring programme. The model was initially developed using artificial data, which mimics typical condition monitoring failure data obtained from the Port Talbot HSM. It assumes that the failure pattern can be divided into two distinct phases: stable and unstable, which can be distinguished between by the use of a statistical process control method. Depending on the progress of failure, one of two models is used to predict the remaining machine life. The first is based on a reliability model while the second uses a novel combination of reliability and condition monitoring measurements. A series of failure case studies, based on actual HSM failures, and a plant trial are used to test and verify the model's prediction performance. The applicability of the model to predict the useful life of a machine, optimise the time to repair/replace equipment and a potential cost modelling strategy are discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (D.Eng.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available