Beyond the diagnosis: the forecast of state system Application in an induction machine

Abstract

This paper deals with the tracking and the prediction of the evolution of the system operation. The aim is to define a forecast of future operating state of the process by using the previous state. First of all, a signature is determined in order to monitor the evolution of different operating modes. For this purpose, on the example of an induction machine, diagnostic features are extracted from current and voltage measurements without any other sensors. Then, a feature selection method is applied in order to select the most relevant features which define the representation space. A polynomial approach of tracking evolution is presented. Next, a Kalman algorithm is developed to predict evolution and to allow pre-empting on the appearance of a fault and the accelerated ageing of system. Finally these two approaches are applied and compared with an induction machine of 5.5 kW with squirrel-cage.