Vibration response of rotating machines is typically mixed and corrupted by a variety of interfering sources and noise, leading to the necessity for the isolation of the useful signal components. A relevant frequently encountered industrial case is the need for the separation of the vibration responses of the same type of bearings inside the same machine. For this purpose, a Blind Source Separation procedure has been successfully applied, based on the maximization of the information transferred in a neural network structure. Thus, a key element for the success of the proposed procedure is the non-linear function used in this single layer Neural Network structure. For this reason, since the vibration response of defective rolling element bearings is characterized by signals with super-Gaussian distributions, a flexible form of this function is used in this paper. The results from an experimental test rig indicate that the performance of the method is insensitive to the form of this function.
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