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We use learning methods to build classiÿers in using a set of training samples. A classiÿer is capable to assign a new sample into one of the diierent learnt classes. In a non-stationary work environment, a classiÿer must be retrained every time a new sample is classiÿed, to obtain new knowledge from it. This is an impractical solution since it requires the(More)
This article presents a plastic injection moulding monitoring module based on knowledge built on-line using feedback from production data. A fuzzy classifier was especially developed for this application. It is based on unsupervised and supervised classification methods. The role of the first one is to identify the functioning modes of the process whereas(More)
The production of maximum amount of electrical power from wind requires the improvement of wind turbine reliability. The life duration and the good functioning of the wind turbine depend heavily on the reliability of its blades. Thus, a critical task is to detect and isolate faults, as fast as possible, and regain optimal functioning in the shortest time.(More)
In this paper, we propose to use the evidence classification method Fuzzy Pattern Matching (FPM) to realize the diagnosis in real time. Then we show how the integration of the incremen-tal learning in FPM allows to complete the missing knowledge in the database and to follow, in real time, the changes on the shape of classes. Finally we develop the(More)