Improving the quality of technical data for developing case based reasoning diagnostic software for aircraft maintenance

Summary form only given. Time spent by airline maintenance operators to solve engine failures and the related costs (flight delays or cancellations) are a major concern to SNECMA which manufacture engines for civilian aircraft such as BOEING 737s and Airbus A340s. The use of an intelligent diagnostic software contributes to improving customer support and reduces the cost of ownership by improving troubleshooting accuracy and reducing airplane downtime. However, classical rule based or model based expert systems are costly to develop and maintain. Our goal has been to improve the development of troubleshooting systems through case based reasoning (CBR) and data mining. These technologies reason from past cases, whose solution is known, rather than rules. New problems are solved by searching for similar problem solving experiences and by adapting the solutions that worked in the past, Our second objective was to acquire the capacity to produce systems which match the quality standard in the aeronautic industry in the given time frame. We aim at both assuring the quality of the core data mining and CBR software as well as the quality of the technical information that is fed into the system (case knowledge)