A self learning neural network for detecting anomalies of a gear system

A Self Learning Neural Network was designed with the scope of denoising and detecting the anomalies (i.e., spikes) on the signals obtained by a simulation of a system gear. In order to check the ability of the network to detect and to put in evidence the anomalies, random white noise was added to the original signal. The spikes were generated by simulating the fatigue crack of one tooth during the rotation of a gear system. Finally, the results of the network were compared to the ones obtained by decomposing orthogonally the signals by means the wavelet transform, of which the ability of investigating on such anomalies is well known.