Modelling and improving maintenance decisions:Having foresight with simulation and artificial intelligence

Machine breakdowns are one of the main sources of disruption and throughput fluctuation in highly automated production facilities. One element in reducing this disruption is ensuring that the maintenance team responds correctly to machine failures. It is, however, difficult to determine the current practice employed by the maintenance team, let alone suggest improvements to it. 'Knowledge based improvement' is a methodology that aims to address this issue, by (a) eliciting knowledge on current practice, (b) evaluating that practice and (c) looking for improvements. The methodology, based on visual interactive simulation and artificial intelligence methods, and its application to a Ford engine assembly facility are described. Copyright © 2002 Society of Automotive Engineers, Inc.

State-of-the-art in integrated vehicle health management

Cranfield University in collaboration with The Boeing Company have set up a Centre of Excellence in IVHM on the University?s technology park. Sponsored by the East of England Development Agency (EEDA), the Centre carries out pre-competitive research and development of IVHM technologies for the benefit of industrial partners. In addition, the dedicated facilities and university staff provide an unparalleled educational environment for learning and applying IVHM technologies. Boeing is actively involved in the creation and work of the Centre through its enterprise-wide Phantom Works technology organization. This paper will describe the organisation and operation of the Centre and will illustrate its activities by describing a research project being carried out in the Centre. This project is a demonstration of an end to end IVHM system beginning with cost/benefit analysis and extending to maintenance, logistics and operations decision support.