Longwall face roof disaster prediction algorithm based on data model driving

Abstract

AbstractHydraulic support is the primary equipment used for surrounding rock control at fully mechanized mining faces. The load, location, and attitude of the hydraulic support are important sets of basis data to predict roof disasters. This paper summarized and analyzed the status of coal mine safety accidents and the primary influencing factors of roof disasters. This work also proposed monitoring characteristic parameters of roof disasters based on support posture-load changes, such as the support location and support posture. The data feature decomposition method of the additive model was used with the monitoring load data of the hydraulic support in the Yanghuopan coal mine to effectively extract the trend, cycle period, and residuals, which provided the period weighting characteristics of the longwall face. The autoregressive, long-short term memory, and support vector regression algorithms were used to model and analyze the monitoring data to realize single-point predictions. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models were adopted to predict the support cycle load of the hydraulic support. The SARIMA model is shown to be better than the ARIMA model for load predictions in one support cycle, but the prediction effect of these two algorithms over a fracture cycle is poor. Therefore, we proposed a hydraulic support load prediction method based on multiple data cutting and a hydraulic support load template library. The constructed technical framework of the roof disaster intelligent prediction platform is based on this method to perform predictions and early warnings of roof disasters based on the load and posture monitoring information from the hydraulic support.