Know-How Retention and Divulgation with a Fuzzy CBR System

This paper describes how Fuzzy Logic helped the representation and handling of equipment fault de- scriptions and diagnosis in a Case-Based Reasoning (CBR) system, in a way close to common spoken lan- guage, 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 cal- culus 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.