- Latest Accepted
- Current Issue
- Online First 2024, Volume 50, Issue 6 Cover Catalogue Previous Issue
Online First have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
2024, 50(6): 1-5, 22.
doi: 10.13272/j.issn.1671-251x.18203
Abstract: The current national standard GB/T 3836.1-2021 Explosive atmospheres-Part 1: Equipment-General requirements and the international standard IEC 60079-0:2017 Explosive atmospheres-Part 0: Equipment-General requirements stipulate that the threshold power of a radio transmitter is the product of the effective output power of the radio transmitter and the antenna gain. Under the condition of a certain threshold for the safe transmission power of radio wave explosion-proof, the larger the antenna gain, the smaller the effective output power of the radio transmitter. This will limit the improvement of wireless transmission distance by increasing the antenna gain. Therefore, it is necessary to study the correctness of the threshold power specified in the national standard GB/T 3836.1-2021 and the international standard IEC 60079-0:2017, and propose reasonable explosion-proof requirements and detection methods for radio wave transmission power. It has been proposed that the safe transmission power of radio waves is independent of antenna gain, and the threshold power of radio transmitters specified in the national standard GB/T 3836.1-2021 and the international standard IEC 60079-0:2017 is incorrect. It is proposed that the threshold for the safe transmission power of underground wireless radio waves in coal mines should be greater than 16 W and independent of antenna gain. The national standard GB/T 3836.1-2021 and the international standard IEC 60079-0:2017 stipulate that the threshold power shall not exceed 6 W, which is incorrect. A method for detecting the explosion-proof safety performance of wireless radio waves has been proposed. The method detects the output power of wireless transmitters. This not only ensures the explosion-proof safety of the detected wireless equipment, but also simplifies the detection method. The method improves the wireless radio wave transmission power of the wireless equipment, removes the limitation on antenna gain, and greatly improves the wireless transmission distance of wireless explosion-proof equipment in coal mines.
2024, 50(6): 6-15.
doi: 10.13272/j.issn.1671-251x.2024040064
Abstract: In order to overcome the problem that a single information source cannot accurately characterize the evolution features of mining landslide disasters, based on multi-source information fusion technology, this paper summarizes the research progress of mine slope landslide disasters from three aspects: multi-source information acquisition of mine slopes, multi-source information fusion of mine slopes, and mine slope displacement prediction and landslide risk assessment. The study summarizes typical slope monitoring methods of "sky", "air", and "ground" , as well as integrated collaborative monitoring method of "sky-air-ground". The study sorts out the slope multi-source information fusion process that includes data level, feature level, and decision level fusion. The paper organizes the fusion forms of displacement and stress, displacement and hydrological and meteorological monitoring information, as well as other different types. This paper elaborates on the current research status of slope displacement prediction and landslide risk assessment based on multi-source information fusion. The accuracy of disaster analysis in current research on mine slope landslide disasters heavily depends on the quality of monitoring data and insufficient utilization of knowledge of rock mechanics mechanisms. Based on the above problems, the development trends of research on landslide disasters in mine slopes are pointed out. The multi-source data collection and access standards are unified. The method for analyzing landslide disasters in mine slopes is developed by integrating monitoring data with rock mechanics mechanisms. The spatiotemporal association mining algorithm for multi-source information from the "sky-air-ground" is optimized. The construction of a mine slope landslide disaster warning platform based on multi-source information fusion is strengthened.
2024, 50(6): 16-22.
doi: 10.13272/j.issn.1671-251x.2024040042
Abstract: The existing sealing methods for goaf along gob-side entry retaining mainly focus on building sealing walls and sealing wall cracks. The construction period is long and repeated, which consumes a lot of labor costs, has a low degree of automation, and is prone to secondary damage. In order to solve the above problems, an automatic sealing system for goaf along gob-side entry retaining has been designed. The system uses flexible sealing airbags as carriers, placing uninflated airbags between the sealing wall and individual hydraulic pillars in the goaf. The system inflates the airbags to make them in contact with the roof and floor of the goaf and the outer side of the sealing wall in the goaf. The intelligent perception of mine pressure causes deformation of the surrounding rock of the roadway, and the shape of the airbag changes flexibly at any time. That is, when the internal pressure of the airbag rises and exceeds the rated pressure of the safety relief valve, it automatically releases the airbag gas to reduce the volume. The airbag re tightly adheres to the roof and floor surrounding rock. It achieves the effect of continuous sealing of the goaf and suppresses the leakage of dangerous gases in the goaf. The on-site test results show that the safety relief valve opens normally when the internal pressure of the flexible sealing airbag reaches about 4 kPa and stops venting when it reaches about 2.7 kPa. Flexible sealing equipment can sense changes in pressure and shrink its volume to adapt to the shape of surrounding rock, allowing for long-term and sustained sealing of goaf. After the installation of flexible sealing equipment, it has a high degree of adhesion with the sealing wall in the goaf. The volume fraction of gas in front of the sealing wall is reduced by 0.13%, effectively suppressing gas overflow.
2024, 50(6): 23-29, 45.
doi: 10.13272/j.issn.1671-251x.2023090004
Abstract: Due to the disturbance of geological disasters such as deep mining and rock burst, there are problems such as poor self perception capability, weak intelligent anti impact adaptive capability, and lack of decision-making and control capability in the advanced support system of the mine. In order to solve the above problems, a adaptive impact resistance support method for advanced hydraulic supports in mines based on digital twins and deep reinforcement learning is proposed. By sensing the roadway environment and advanced hydraulic support status through multiple sensors, a digital twin model of a physical entity is created in a virtual world. The physical model accurately displays the structural features and details of the advanced hydraulic support. The control model realizes adaptive control of the advanced hydraulic support. The mechanism model realizes logical description and mechanism explanation of the adaptive support of the advanced hydraulic support. The data model stores the physical operation data and twin data of the advanced hydraulic support. The simulation model completes the simulation of the advanced hydraulic support column to achieve virtual real interaction between the advanced hydraulic support and the digital twin model. According to the adaptive impact resistance decision-making algorithm based on deep Q-network (DQN) for advanced hydraulic support, intelligent decision-making is made for roadway impact resistance support in the simulation environment. Based on the decision results, control instructions are issued to physical entities and digital twin models to achieve intelligent control of advanced hydraulic support. The experimental results show that the displacement and pressure changes of the column are consistent, indicating that the simulation model design of the advanced hydraulic support column is reasonable, thereby verifying the accuracy of the digital twin model. The adaptive impact resistance decision-making algorithm for advanced hydraulic supports in mines based on DQN can adjust the PID parameters of the hydraulic support controller, adaptively regulate the column pressure, improve the safety level of roadways, and achieve adaptive impact resistance support for advanced hydraulic supports.
2024, 50(6): 30-35.
doi: 10.13272/j.issn.1671-251x.2023120009
Abstract: Currently, most multi-step hydraulic support pressure predictions are cumulative predictions of single step hydraulic support pressure. The more times a single step accumulates, the greater the cumulative error, which affects the prediction precision. In order to solve the above problems, a spatiotemporal multi-step prediction method of hydraulic support pressure based on long short term memory (LSTM)-Informer model is proposed. After using Kalman filtering to eliminate vibration noise in hydraulic support pressure data, two spatiotemporal datasets (Dataset 1 and Dataset 2) are established by selecting 5 adjacent hydraulic support pressure data at the end and middle of the working face. The spatiotemporal data is standardized and preprocessed. The method inputs spatiotemporal data into the LSTM model to extract spatiotemporal features, and inputs the extracted spatiotemporal features into the encoder of the Informer model. After position encoding, the method outputs multi head probability sparse self attention to focus on the changing features of the pressure sequence. After maximum pooling and one-dimensional convolution, the method eliminates the redundant combination of output feature map. By utilizing multi head probability sparse self attention to further focus on pressure sequence features, the decoder of the Informer model is changed to a fully connected layer to obtain the prediction results of hydraulic support pressure. The experimental results show that compared with prediction methods based on gated recurrent unit (GRU), LSTM, and Informer models, prediction methods based on LSTM-Informer model has the smallest root mean square error (RMSE) and mean absolute error (MAE) in predicting hydraulic support pressure at 6, 12, and 24 step sizes. The RMSE of the 6-step hydraulic support pressure predicted based on dataset 1 decreases by 41.63%, 49.74%, and 11.85%, and the MAE decreases by 41.75%, 50.00%, and 12.00%, respectively. The RMSE of the 6-step hydraulic support pressure predicted based on dataset 2 decreases by 48.15%, 59.86%, and 19.88%, and MAE decreases by 49.87%, 54.90%, and 13.16%, respectively.
2024, 50(6): 36-45.
doi: 10.13272/j.issn.1671-251x.2024030037
Abstract: Currently, most fiber optic microseismic monitoring systems in China are based on optical grating sensing technology. However, fiber optic grating wavelength demodulation limits the detection frequency and sensitivity of the system, and there are few successful cases of long-term, continuous and uninterrupted microseismic monitoring. In order to solve the above problems, a new type of all fiber microseismic monitoring system is proposed. Taking the monitoring of water inrush from the floor during the mining process of Pan'er Coal Mine 11023 working face as the engineering background, a comparison is made between the all fiber optic microseismic monitoring system and the ESG microseismic monitoring system. It is found that the all fiber optic microseismic monitoring system has the following advantages. The recorded waveform spectrum features are clearer, showing a high signal-to-noise ratio advantage. The monitoring range for disturbance depth is larger, and the remote monitoring effect is better. The distribution of seismic source positioning results is more reasonable and more in line with the actual mining situation of the working face. During the monitoring of the entire mining cycle of the working face, the relationship between the floor failure and microseismic activity in the fault abnormal area of the 11023 working face is analyzed. Near the fault and coal seam thinning abnormal area, the number and intensity of microseismic events increase. During the initial mining period of the working face, stress is concentrated and released. Due to the influence of mining, the floor is severely damaged. Relatively high energy events are mainly distributed in the floor of the fault anomaly area, with a depth of about 27 meters of damage to the floor. Micro seismic events do not form a line or accumulate in a plane below 60 meters of the 3 coal seam floor. It indicates that cracks have not expanded and no water conducting channels have been formed. The working face can be safely mined.
2024, 50(6): 46-53, 60.
doi: 10.13272/j.issn.1671-251x.2024010094
Abstract: The advanced seismic-while-excavating detection technology can achieve parallel exploration and excavation, providing the possibility of real-time and accurate geological support in the scenario of rapid and intelligent excavation of roadways. The signals generated by the excavation seismic source are complex, variable frequency, and continuous. The recognition of signal features directly affects the accuracy of data processing and imaging. However, currently, the recognition of seismic-while-excavating signal features for rock tunnel boring machine (TBM) is still unclear, and there is currently no targeted research on signal processing and imaging. In order to solve the above problems, taking the TBM advanced seismic-while-excavating detection test of the gas control roadway in Xieqiao Coal Mine as an example, the time domain, frequency domain, and frequency domain features of the cutterhead pilot signal and the rock wall received signal are analyzed. The proportion of different amplitude energy components in the rock roadway TBM seismic-while-excavating signal show a pyramid shape. But the distribution is random and the degree of asymmetry is high. The energy of the mechanical operation signal is relatively high, and the strength of the cutterhead pilot signal is about 200 times that of the signal received by the rock wall. The frequency domain frequency conversion features are obvious. The basic frequency of the mechanical operation signal is relatively low, and the frequency components of the cutterhead pilot signal are mainly concentrated in the range of 10-80 Hz and 150-200 Hz, with a main frequency of 36.99 Hz. The frequency components of the rock wall received signal are mainly concentrated in the range of 50-200 Hz, with a main frequency of 137.97 Hz. The frequency domain energy distribution of the cutterhead pilot signal is more regular than that of the rock wall received signal, and the phenomenon of multiple source excitation is obvious. The difference features between energy clusters indicate the randomness of amplitude energy and duration during multiple source excitations. The data processing and imaging experiments of TBM seismic-while-excavating signals in rock roadways are carried out using the pulse algorithm and diffraction stacking migration imaging method. The results show the following points. ① The pulse equivalent single shot record has strong consistency with the advanced detection single shot record obtained from conventional seismic-while-excavating sources, with clear and continuous in-phase axes, which can meet the needs of on-site detection analysis.② The advanced prediction results of the rock mass situation within the detection range are consistent with the actual exposure, indicating that TBM advanced seismic-while-excavating detection in rock roadways can provide effective geological support.
2024, 50(6): 54-60.
doi: 10.13272/j.issn.1671-251x.2023120032
Abstract: At present, the decision-making of coal mine roof disaster prevention and control measures and the analysis of accident causes mainly rely on manual experience, and the level of intelligence is relatively low. The knowledge graph of roof disaster prevention and control can integrate knowledge and experience of roof disaster prevention and control, assist in analyzing the causes of roof disaster accidents and making decisions on roof disaster prevention and control measures. A method for constructing a knowledge graph of coal mine roof disaster prevention and control has been proposed. The ontology method is used to complete the knowledge modeling of coal mine roof disaster prevention and control. The concepts in the field of roof disaster prevention and control are divided into mine geology, mining technology, prevention and control measures, and accident characterization. The relationships between concepts are defined as usage, triggering, susceptibility, control, prevention, and applicability. The knowledge modeling lays the foundation for the knowledge extraction of coal mine roof disaster prevention and control (entity extraction and relationship extraction). Based on the characteristics of entity overlapping between a large number of nested entities and relationships in the field of coal mine roof disaster prevention and control, a span based entity extraction method and a dependency syntax tree guided entity representation based relationship extraction method are determined. The method constructs a corpus in the field of roof disaster prevention and control, and uses the Neo4j graph database to store data, providing data source support for the application of knowledge graph of roof disaster prevention and control. The partial construction results of the knowledge graph of coal mine roof disaster prevention and control are displayed. It indicates that this knowledge graph can assist in the analysis of roof disaster accident causes and decision-making of prevention and control measures, thereby improving the intelligence level of roof management. It is pointed out that based on this knowledge graph, combined with natural language processing and knowledge reasoning technologies, knowledge Q&A on roof management can be achieved.
