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Neuro-fuzzy diagnosis in final control elements of AC motors

The on-line diagnosis of an electrical drive is the first step towards the application of an effective predictive maintenance plan on such electrical machines. This paper applies neuro-fuzzy logic to induction motors condition monitoring. The analysis of residual features corresponding to instrumentation faults of induction motor lead to the design of faults classification system. The training set is obtained by a faulted machine dynamical model as simulator. Two neuro-fuzzy structures are conceived to learn the exact input-output relation of the fault detection process for induction motor using measured data. The first neuro-fuzzy architecture maps the residuals into two classes: a one of fixed direction residuals and another one of faults belonging to velocity sensor. The second adaptive neuro-fuzzy network can be able to provide updated membership functions of the sets of fixed oriented residuals that better describe the fault diagnosis map. The preliminary results show that neuro-fuzzy logic can be used for accurate induction motors fault diagnosis if the input data are processed in an optimized way.