Using linear interpolation and Kalman prediction in Pattern Recognition: Application to an induction machine

This paper deals with pattern recognition (PR) method associated with a tracking and a prediction of evolution for various operating modes of a process. The aim is to improve diagnosis of a process by enhancing its knowledge database. Indeed, PR needs an initial database named training set. It is composed of different operating modes and obtained during the first step of PR. It is commonly named training phase. It is a laborious step and moreover the whole of operating modes is never available (generally poor experimental feedback). Thatpsilas why, using knowledge in training set, it is interesting to predict evolution of operating modes in unknown fields of representation space. PR steps are first presented and followed by a polynomial approach of tracking evolution. Next, a Kalman algorithm is used to predict evolution and finally two different asynchronous machines (5.5 kW and 18.5 kW) are used to illustrate our purpose.