The acoustic emission (AE) events exhibit significant characteristics at different loading stages, providing valuable insights into the initiation, propagation, connectivity, and fracture of microcracks throughout the entire damage process of coal. The study organizes the AE events generated during loading into chronological order and utilizes the autoregressive integrated moving average (ARIMA) model to predict the damage evolution of the coal mass during future loading time, which can provide a reference for early warning and protection of coal rock mass instability and failure. The results indicated that the AE phased evolution characteristics during the loading process are not affected by variations in confining pressure. The AE event rate curves undergo a progressive transition from the “quiet period” to the “non-quiet period” upon entering the pre-peak plastic deformation stage. The time series autocorrelation and non-stationary fitting results for the AE event rate during the loading process show that the AE event rate before coal instability and failure under different confining pressures shows a significant sharp increase and decrease phenomenon. The abnormal behavior during this stage is used as a precursor to predicting the instability and failure of the coal mass. Furthermore, based on the AE events generated during the stable crack propagation stage, a real-time dynamic warning method for coal and rock dynamic disasters is proposed based on the ARIMA model, offering a tool for predicting the instability and failure of coal and rock mass.Highlights<UnorderedList Mark="Bullet"> <ItemContent> The evolution law of acoustic emission (AE) event gradually transitions from the “quiet period” to the “non-quiet period” during the pre-peak plastic deformation stage. </ItemContent> <ItemContent> The AE events generated during the loading process are arranged in chronological order using the time series ARIMA model. The correlation between the AE signal characteristics and the loading time is used to predict the phased deformation and failure characteristics. </ItemContent> <ItemContent> The ARIMA model is employed to propose a real-time dynamic warning method for coal and rock dynamic disasters, which is designed to foresee the instability and failure of coal and rock mass. </ItemContent> </UnorderedList>

Study on the Prediction Method for Coal and Rock Mass Fracture Process Based on Time Series Acoustic Emission Statistical Model

期刊论文
发表日期：

2025年4月4日

查看全文

Qi Zhang,
Jian Pei,
Yaoru Liu,
Xiangchun Li,
Baisheng Nie,
Junwen Zhang,
Hongtao Liu

摘要

The acoustic emission (AE) events exhibit significant characteristics at different loading stages, providing valuable insights into the initiation, propagation, connectivity, and fracture of microcracks throughout the entire damage process of coal. The study organizes the AE events generated during loading into chronological order and utilizes the autoregressive integrated moving average (ARIMA) model to predict the damage evolution of the coal mass during future loading time, which can provide a reference for early warning and protection of coal rock mass instability and failure. The results indicated that the AE phased evolution characteristics during the loading process are not affected by variations in confining pressure. The AE event rate curves undergo a progressive transition from the “quiet period” to the “non-quiet period” upon entering the pre-peak plastic deformation stage. The time series autocorrelation and non-stationary fitting results for the AE event rate during the loading process show that the AE event rate before coal instability and failure under different confining pressures shows a significant sharp increase and decrease phenomenon. The abnormal behavior during this stage is used as a precursor to predicting the instability and failure of the coal mass. Furthermore, based on the AE events generated during the stable crack propagation stage, a real-time dynamic warning method for coal and rock dynamic disasters is proposed based on the ARIMA model, offering a tool for predicting the instability and failure of coal and rock mass.

Highlights

The evolution law of acoustic emission (AE) event gradually transitions from the “quiet period” to the “non-quiet period” during the pre-peak plastic deformation stage.

The AE events generated during the loading process are arranged in chronological order using the time series ARIMA model. The correlation between the AE signal characteristics and the loading time is used to predict the phased deformation and failure characteristics.

The ARIMA model is employed to propose a real-time dynamic warning method for coal and rock dynamic disasters, which is designed to foresee the instability and failure of coal and rock mass.