Research on coal mine longwall face gas state analysis and safety warning strategy based on multi-sensor forecasting models.

Intelligent computing is transforming safety inspection methods and response strategies in coal mines. Due to the significant safety hazards associated with mining excavation, this study proposes a multi-source data based predictive model for assessing gas risk and implementing countermeasures. By examining the patterns of gas dispersion at the longwall face, utilizing both temporal and spatial correlation, a predictive model is crafted that incorporates safety thresholds for gas concentrations, four-level early warning method and response strategy are devised by integrating weighted predictive confidence with these correlations. Initially tested using a public dataset from Poland, this method was later verified in coal mine in China. This paper discusses the validity and correlation of multi-source monitoring data in temporal and spatial correlation and proposes a risk warning mechanism based on it, which can be applied not only for safety warning but also for regulatory management.

Underground abnormal sensor condition detection based on gas monitoring data and deep learning image feature engineering.

Underground gas sensors as the most intuitive tool for monitoring gas concentrations in underground mining, yet they are subject to frequent anomalies due to ground pressure, constructions, even malicious masking by workers. Due to the depth of underground mining and the complexity of the environment, it is almost impossible to manually monitor the status of the all the sensors. Thus, the ability to accurately identify the working status of gas sensors at the working face are critical importance to mining safety. In this paper, we propose a deep learning feature engineering based approach to coupling the relationship between underground sensors. Experiment results show that the relationship between gas sensors can be expressed by position and time, so that when a sensor such as upper corner T0 malfunctions, it can be detected by other sensors such as T1 and T2. By converting the gas concentration into the form of recurrence plots (RPs), we are able to transform time-series gas concentration data into images with more dimensions in time lag, and enabling the application of more efficient and accurate machine vision methods. Based on the location of sensors at the working face, we found that the sensors at positions T0, T1 and T2 are correlated as the wind flows through the tunnel and have a higher correlation in the subsections of the time series. And those correlation can directly use to check the operating status of the sensors. We also discuss whether the relationships between the data itself can be preserved at the feature level during the mapping of gas concentrations to features, since deep learning (DL) looks like the next promise future after digitization in the mining industrialization with more and more data analysis and placing the results under a larger decision. This feature-based approach for gas concentration analysis can also be used for prediction and early warning.

Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model.

In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm's (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.