In this paper, a pattern recognition (PR) method is used to provide the tracking and the diagnosis of a system. First of all, from measurements carried out on the system, features are extracted from current and voltage measurements without any other sensors. These features are used to build up a pattern vector, which is considered as the system signature. Then, a feature selection method is applied in order to select the most relevant features, which define the representation space. The decision phase is based on the "k-nearest neighbors" (knn) rule, associated with an evolution tracking of system using trajectory allowing a diagnosis not only of states defined in the training set, but also of the intermediate states. The appearance of a new operating mode is taken into account in order to enrich the initial knowledge base and thus to improve the diagnosis. This approach is illustrated on asynchronous motor of 5.5 kW with squirrel cage, in order to detect broken bars under any load level. The experimental results prove the efficiency of PR methods in condition monitoring of electrical machines.

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This paper deals with a diagnosis tool based on a pattern recognition approach associated with Kalman interpolator/extrapolator. The first aim is to decrease the number of measurements to realize while increasing the learning database contents using a Kalman state estimator. The second one is to estimate, from the initial set of measured data, future states of the studied process. A 5.5-kW induction motor bench is used as an application to validate this approach. First, a signature is determined in order to monitor the different operating modes evolution. Diagnostic features are extracted only from current and voltage sensors. Then, a feature selection method is applied in order to select the most relevant features for diagnosis. Finally, a Kalman filter algorithm is developed in order to interpolate the known states and to predict evolution toward new ones. A new diagnosis tool is then designed handling continuous evolution (severity, load) inside the different operating modes (healthy, stator fault, . . .).

Phone: 133 4 72 1 8 6 I 03 -olivier.ondel~,ec-lyon.fr I:%: +33 4 i a 43 37 17Abstract-Electrolytic filter capacitors are Irequently responsible for static converters breakdowns. The rise of temperature within capacitor is the most significant factor upon ageing and so lifespan . To predict the ageing of the capacitor, a method of predictive maintenance has been developed . It consists in monitoring the voltage ripple of the capacitor. This ripple is the hest indicator of its worn state. It is function of parameters such as input voltage, output current and ambient temperature. A reference system taking into account all these parameters is determined Cor sound capacitors. We will deduce that the dispersion of input voltage had a weak influence upon voltage ripple compared to other parameters such as output current and ambient temperature. And precisely the temperature its being an influent parameter in the diagnosis, measurement must he very relevant. That is why we will measure the internal temperature of capacitor which takes into account the ambient temperature hut also the internal heating of capacitor. This measurement will he done in an environment not confined for not which is distorted. We will check then the relevance of our modifications by comparing our results with the actual values of the state of capacitors deduced using a bridge RLC.