Abstract
To the instability of acoustic emission (AE) signal of rock fracture, the method about feature extraction and comprehensive recognition of those was came up with combining AE parameters, EMD and BP neural network. Through the acoustic emission experiment of different brittle rock under uniaxial compression, stress-strain curve and AE data were obtained; time-frequency characteristics of AE signal of rock samples were compared. Feature vectors, like AE parameters, and EMD energy entropy, was synthesized to BP neural network to distinguish different AE signal. The results show that evolution characteristic with stress or time of AE parameters of different rock which was under uniaxial compression exist similarities and differences. EMD and Welch spectrum can reflect the difference among spectrum, energy distribution of AE signal of different rock very well. With various characteristics of different rock acoustic emission, the neural network has good recognition effect.
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