Neural network approach for diagnosis of grinding operation by acoustic emission and power signals

In this study, a neural network technique has been used to achieve an intelligent diagnosis for chatter vibration and burning phenomena on grinding operation. Acoustic emission and power signals were experimentally obtained by means of a multi-sensor method with acoustic emission sensor and power meter. By signal processing methods, signal parameters that influence the grinding state were determined from the acoustic emission and the power. Static power and dynamic power were determined as power parameters, and also peak of RMS and peak of FFT were applied as acoustic emission parameters. These parameters were used as inputs of the neural network to diagnose the grinding faults. According to the substructure of the neural network, the diagnostic performance of the constructed neural network was examined.