Bedding increases coal seam anisotropy, which leads to significant differences in the evolution laws of mining stress and strata movement. This work analyzed the dip angles of different layers to analyze the mechanical properties of the coal seam under unloading. The coal sample was subjected to triaxial compression and unloading damage acoustic emission testing. The brittleness characteristics of the coal sample failure in different bedding directions differed significantly. Compared with axial parallel bedding coal samples, axial vertical bedding and inclined stratification reached an ultimate strength. The stress–strain curve decreased sharply and showed visible brittle-drop characteristics. The average strengths of the axially inclined bedding and the parallel layered coal sample decreased compared with the axial vertical bedding coal sample and were 10.20 and 16.12 MPa, respectively, which implies a greater susceptibility to failure during unloading confining pressure tests. Acoustic emission monitoring indicated that the axial vertical bedding and inclined bedding showed sudden destruction of different coal samples, a reduction in axial parallel bedding ductility coal sample characteristics, and stronger unloading damage on the axis parallel to the bedding coal sample. Further, using the acoustic emission ring count rate and the cusp catastrophe theory, the unloading failure prediction of coal samples is carried out. The prediction results are not different from the experimental results, which shows that this method is feasible.

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.

In the study of the acoustic emission (AE) characteristics of rock samples or coal samples under triaxial compression conditions, most scholars carry out relevant experiments by placing the AE detector on the outer wall of the triaxial chamber of the rock mechanics test system. Owing to the continuous obstruction of AE signals by hydraulic oil in the triaxial chamber and the frequent interference of external noises, the final experimental data cannot objectively and truly reflect the essential characteristics of AE of rock or coal under triaxial compression conditions. It is difficult to scientifically guide and accurately predict precursory information of rock’s or coal’s rupture and instability. Based on this, a series of improvements and optimizations were made to the original triaxial compression AE test method, which is based on the modification of the communication interface of the rock mechanics test system, a test head which can put the AE detector into the triaxial chamber and withstands high confining pressure, in order to obtain the true, comprehensive, and reliable AE signals. It is of considerable significance to the scientific determination of the precursory characteristics of rock’s or coal’s rupture and instability.