Know-How Retention and Divulgation with a Fuzzy CBR System

This paper describes how Fuzzy Logic helped the representation and handling of equipment fault descriptions and diagnosis in a Case-Based Reasoning (CBR) system, in a way close to common spoken language, frequently vague, used by maintenance teams in daily maintenance tasks. These descriptions may be of two types as sometimes they contain a judgment based on the technician own know-how and sometimes they don't. For case similarity computation the "meaning" of these observations must be understood and all must be reduced to the same format before any further calculus may take place. In this context another important issue is relevance: the weight of each observation for each possible diagnosis is automatically captured by the CBR system according to the experiences it stores along its life-cycle. A prototype has been tested in the health-equipment maintenance field. The system acts as a know-how repository and a divulgation support, part of an intelligent e-learning platform for staff training.