Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis.

Acoustic emission information can describe the damage degree of rock samples in the process of failure. However, as a discrete non-stationary signal, acoustic emission information is difficult to be effectively processed by conventional methods, while wavelet analysis is an effective method for non-stationary signal processing. Therefore, acoustic emission signal is deeply studied by using wavelet analysis method. In this paper, on the basis of noise reduction of acoustic emission signal, Matlab calculation program is used to decompose the acoustic emission signal of coal sample under the confining pressure test of triaxial unloading, and the singularity detection is carried out. The results show that the time when the Lipschitz index value first appears α negative can be used as the prediction time. However, the corresponding time when the Lipschitz index value is -0.15~-0.31 should be excluded; The absolute range of the difference between the final forecast time and the actual rupture time of coal samples is [5.2s, 17.1s], and the coal samples with the absolute value of time difference within [5.2s, 10.0s] account for 63.6% of the total.