Research Of Mechanical Fracture Location Based On Acoustic Emission

With the development of acoustic emission and signal processing technology, the related scholars’ research on acoustic emission phenomena of metal has making acoustic emission widely used in the field of mechanical fault detection. The acoustic emission testing equipment used today are mostly based on PC platform to extract the characteristic parameters of acoustic emission signals. But the equipment always dropout useful intensive sound source signal. And the research for sound source localization of mechanical failure is not enough. So in this paper we designed an acoustic emission detector based on the embedded platform, and analyzed the characteristic parameter extraction and source localization.The mechanism of metal material released acoustic emission waves is described in this paper, the time-domain and frequency-domain characteristics of acoustic emission signals and the meaning as well as formula of characteristic parameters are introduced. Also the most commonly principles based on time-difference are described, such as linear positioning, planar positioning and three-dimensional positioning. Analog bandpass filter for metal material is designed according to the character of various aspects of the acoustic emission system being easy to introduce noise. Precise and efficient state machine is achieved based FPGA to extracting the characteristic parameters, which exerts the advantages of FPGA hardware platform. A Chan positioning algorithm based on outlier analys is proposed. Finally, components are selected and hardware platform based on FPGA and STM32 is established through demonstration program, also realization ideas of feature extraction of Verilog HDL code and the localization algorithm C code are illustrate in detail.The system can complete a 4-channel data acquisition, real-time feature extraction and the acoustic emission source location by Modelsim timing simulation and data comparison with the actual instrument, in order to verify the de-noising effect of the analog filter, the efficiency of feature extraction algorithm and the accuracy in acoustic emission source location. The research result is useful to mechanical failure detection and development of special equipment.