Guillermo R. Bossio

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A scheme for diagnosis and identification of mechanical unbalances and shaft misalignment on machines driven by induction motors is presented in this work. Fault identification is performed using unsupervised artificial neural networks: the so-called Self-Organizing Maps (SOM). The information of the motor phase current is used for feeding the network, in(More)
Broken rotor bars in induction motors can be dependably detected by analyzing the current signatures under sufficient motor load conditions. Detection becomes less dependable under light motor load conditions. There are also cases in which tolerable motor operating conditions generate current signatures similar to those of motors with broken rotor bars.(More)
In this paper, the analysis and validation of a dynamic model for Permanent Magnet Synchronous Machines (PMSMs) with stator faults is presented. A proper dynamic model including stator faults is required in order to propose and validate model-based fault detection strategies. Unlike other models which include a stator fault in one of the phase windings, we(More)
In this paper, a new strategy for interturn short-circuit fault detection and isolation in Permanent Magnet Synchronous Machines (PMSMs) is proposed. The strategy is based on the measurement of PMSM currents and voltages. The fault detection for any phase of the stator windings is performed from the analysis of a “vector residual” generated by a state(More)