Self organizing maps for monitoring parameter deterioration of DC and AC motors

A novel method for the detection of faults in DC and AC motors using Kohonen Networks (Self Organized Maps) is presented in this paper. The advantage of this technique is that it only requires input samples to perform the training of the network, a difference from other Neural Network architectures that need both inputs and outputs to perform the training. This technique generates fault maps based only on the inputs that are received from the motor under test. The maps can be used to clearly identify different types of faults in DC and AC motors since similar faults generate the same type of map. The main advantages of this technique are that it can be used to test motors in real time and on-site, without having to disconnect the motor for testing. The technique can be applied for testing motors used in production lines without having to stop operation for testing.