Signal based approach for data mining in fault detection of induction motor

The aim of this paper is to introduce a new method which combines data mining and signal processing techniques for identifying potential faults in electric motors. The vibration signals measured in the initial (healthy) state of the electric motor are used as source data for application of data mining technique. In this sense, a new data mining technique is introduced by the definition of a feature transfer function application which is best on the Continuous Wavelet Transform. Hence it constitutes a blind algorithm which can extract the features that are hidden in the data and also all characteristic features are detected by an auto associative neural network from the error variation.