Fault diagnosis on induction motors using Self-Organizing Maps

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 order to perform the fault diagnosis. The network is trained using data generated through the simulation of a motor-load system model. Such model allows including the effects of load unbalance and shaft misalignment. Experimental data are later applied to the SOM in order to validate the proposal. It is demonstrated that the strategy is able to correctly identify both unbalanced and misaligned cases.