Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer.

To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital-model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.

Selection of longwall shearers based on the results of research on the mechanical properties of coal.

The proper selection of cutting longwall winning machines for specific mining and geological conditions requires the development of an appropriate algorithm. The appropriate selection of a machine (shearer) is closely related to acquiring a high concentration of exploitation from the given longwall. That is indispensable, especially taking into consideration the growing cost of mining, as well as the depth of coal seams. In this article, an algorithm showing the selection of longwall winning machines has been presented. The algorithm has been created based on results of research on the processed coal's mechanical properties. Analysis of the mining process, especially in difficult conditions, shows that in order to define a drum longwall shearer's range of usage, the coal's properties which have a significant impact on the mining process must be determined. The above also influences the technique, technology and effectiveness of mining-they impact the effectiveness of the winning machines. In connection to this, the cutting heads should be chosen and designed based on those factors, as well as any performance forecasts. As representative values the following have been chosen: workability index (WUB), resistance to unidirectional compression (Rc), and energy consumption of the mining process (TE).

Design of Digital Twin Cutting Experiment System for Shearer.

This study presents an advanced simulated shearer machine cutting experiment system enhanced with digital twin technology. Central to this system is a simulated shearer drum, designed based on similarity theory to accurately mirror the operational dynamics of actual mining cutters. The setup incorporates a modified machining center equipped with sophisticated sensors that monitor various parameters such as cutting states, forces, torque, vibration, temperature, and sound. These sensors are crucial for precisely simulating the shearer cutting actions. The integration of digital twin technology is pivotal, featuring a real-time data management layer, a dynamic simulation mechanism model layer, and an application service layer that facilitates virtual experiments and algorithm refinement. This multifaceted approach allows for in-depth analysis of simulated coal cutting, utilizing sensor data to comprehensively evaluate the shearer's performance. The study also includes tests on simulated coal samples. The system effectively conducts experiments and captures cutting condition signals via the sensors. Through time domain analysis of these signals, gathered while cutting materials of varying strengths, it is determined that the cutting force signal characteristics are particularly distinct. By isolating the cutting force signal as a key feature, the system can effectively distinguish between different cutting modes. This capability provides a robust experimental basis for coal rock identification research, offering significant insights into the nuances of shearer operation.

Influence of coal cowl parameters on the coal loading process of thin coal seam shearer drum.

The low loading rate of the thin coal seam shearer drum is a severe obstacle to the efficient mining of thin seam resources, and the auxiliary drum loading through the cowl is an effective measure to alleviate this situation. However, the working mechanism of the coal cowl still remains unclear. In this paper, with the help of the discrete element method and the modeling experiment method, the effects of coal cowl's offset distance, tilt angle and wrap angle on the coal loading rate under different loading modes of the drum are investigated; and the significance of various factors and their interactions to the drum coal loading rate is explored by designing response surface experiments. The findings show that a monotonous negative correlation between the offset distance of the coal cowl and the coal loading rate is identified, and that a smaller offset distance can effectively improve the coal loading rate of the drum. The conveying torque is significantly increased, easily inducing the drum choking, coal recycling coal over-crushing. Along with the increasing tilt angle, the rate of ejection loading decreases monotonically, and the rate of pushing loading increases first and then decreases. Coal loading rate is weakly affected by changes in coal cowl's wrap angle. The results of response surface analysis reveal that the most significant factors affecting the drum's coal loading rate are tilt angle and offset distance in ejection and pushing loading modes, respectively. The conclusions drawn here offer implications for improving the coal loading performance of the thin coal seam shearer drum, as well as certain guidance on the optimal design of coal cowl parameters.

Experimental study on shearer traction vibration considering attitude disturbances.

Due to the influence of structural clearances, the shearer's oscillates and jumps concerning the scraper are frequent, which induces the collision and vibration impact of the traction components and exacerbates the traction failure of the shearer. Therefore, to explore the correlation between attitude disturbance and traction vibration, an experiment on the traction vibration is carried out, the spatial swaying of the shearer and vibration differences between two traction components are obtained, the influence of the lifting angle of the rocker arm is discussed, and the influence mechanism of the shearer attitude disturbance on traction vibration is elucidated. The results indicate that the rolling swing intensity of the shearer is the highest while the yawing swing intensity is the lowest, and the pitch swing intensity increases with the increase of the lifting angle of the rocker arm. Besides, the vibration impact indices of the two walking mechanisms have a competitive relationship of one decreasing but the other increasing, which can be used as a reference signal to judge the rolling swing and load-sharing performance of the traction part. Moreover, with the swing attitude, the competitive relationship of the average of vibration peaks is shown in the two support shoes, and it can be used as a reference signal to judge the pitching swing and the load-sharing performance of the traction part. This result reveals the impact mechanism of attitude disturbances on traction vibration and proposes a signal monitoring approach for judging the traction attitude disturbance and load-sharing performance, providing a reference for reducing traction faults.

Experimental Research of Triple Inertial Navigation System Shearer Positioning.

In order to improve the positioning accuracy of shearers, the overground experimental device based on the positioning model of TINS (Triple Inertial Navigation System) was built. The influence of TINS installation parameters on positioning accuracy was discussed through two sets of experiments: the inter-INS (Inertial Navigation System) distances influence experiments and the tri-INS plane spatial position influence experiments. The results show that the positioning accuracy of the shearer is improved to a different extent under the two sets of experimental conditions. When the inter-INS distances are 0.2 m, the positioning accuracy is the highest and the positioning accuracy improvement effect is also the best. When the negative plane α3 is 45°, the positioning accuracy is the highest, and the positioning accuracy improvement effect is also the best. The analysis shows that the main factor affecting the positioning accuracy is the precision of the evaluated values outputs of TINS from EKF (Extended Kalman Filter). Considering the positioning accuracy, equipment installation convenience and so on, the optimum installation parameters are 90° (horizontal installation) α3 for the positive plane and 0.2 m inter-INS distances.

Research on IMU-Assisted UWB-Based Positioning Algorithm in Underground Coal Mines.

The application of an ultra-wideband (UWB) positioning system in a Global Positioning System (GPS) denial environment such as an underground coal mine, mainly focuses on position information and rarely involves information such as direction attitude. Position accuracy is often affected by multipath, non-visible ranges, base station layout, and more. We proposed an IMU-assisted UWB-based positioning system for the provision of positioning and orientation services to coal miners in underground mines. The Error-State Kalman Filter (ESKF) is used to filter the errors in the measured data from the IMU-assisted UWB positioning system to obtain the best estimate of the error for the current situation and correct for inaccuracies due to approximations. The base station layout of the IMU-assisted UWB positioning system was also simulated. The reasonable setting of the reference base station location can suppress multi-access interference and improve positioning accuracy to a certain extent. Numerous simulation experiments have been conducted in GPS denial environments, such as underground coal mines. The experimental results show the effectiveness of the method for determining the position, direction, and attitude of the coal miner under the mine, which provides a better reference value for positioning and orientation in a GPS rejection environment such as under the mine.

Simulation Optimization and Application of Shearer Strapdown Inertial Navigation System Modulation Scheme.

The operating attitude of a shearer based on a three-dimensional (3D) space scale is the necessary basic information for realizing intelligent mining. Aiming to address the problem of the insufficient perception accuracy of shearers, in this paper, the rotation model of the actual turning mechanism of the strapdown inertial navigation system (SINS) of shearers is established, and the error propagation characteristics of different single-axis rotation modulation schemes are revealed. Through theory and simulation, the optimal rotation modulation scheme is determined to be the improved four-position turn-stop modulation with a rotation of <360°. The experiment shows that the 24 h positioning error of this scheme is 3.7 nmile, and the heading angle changes by 0.06°, which proves that this scheme can effectively improve the attitude perception accuracy of the inertial navigation system (INS). The field application of the shearer operating attitude perception based on this scheme shows that the positioning error after error compensation is 17% of that before compensation, and the heading angle error is 75% of that before compensation, which verifies that this scheme can significantly improve the accuracy of shearer operating attitude perception in field applications. This scheme can achieve higher precision perception accuracy based on SINS and has broad application prospects in the field of high-precision pose perception of coal mining machines, roadheaders, and other equipment.

Analyze the Wear Mechanism of the Longwall Shearer Haulage System.

The wear characteristics and related mechanisms of the Longwall Shearer Haulage System were investigated. Wear is one of the main reasons for failures and downtimes. This knowledge can help solve engineering problems. The research was carried out at a laboratory station and a test stand. The publication presents the results of tribological tests carried out in laboratory conditions. The aim research was to select the alloy intended for casting the toothed segments of the haulage system. The track wheel was made by the forging method using steel 20H2N4A. Haulage System was tested on the ground using a longwall shearer. Selected toothed segments were subjected to tests on this stand. The cooperation of the track wheel and toothed segments in the tootbar were analyzed by a 3D scanner. Debris chemical composition was also appointed, as well as mass loss of toothed segments. The developed solution toothed segment an increase in the service life of the track wheel in real conditions. The results of the research also contribute to reducing the operating costs of the mining process.

Research on gear crack fault diagnosis model based on permanent magnet motor current signal.

To solve the problem that it is inconvenient to install additional sensors to detect fault signals on the gear of the shearer, the permanent magnet motor control model was first established, and the motor control parameters were determined. Then, the gear mesh stiffness is used as the main judgment basis for gear failure. The gear failure model and the system torsion dynamics model are established. The gear meshing stiffness curve under the crack fault is fitted. Finally, the transmission system overall dynamic model is proposed to obtain the motor current and gear fault information. The influence of different faults on the system electromechanical performance is studied. The mapping relationship between the crack fault and the current is established. The typical characteristics under fault conditions are extracted, which is an important reference for studying the health status of the cutting drive system in the shearer.

Information Inequalities via Submodularity and a Problem in Extremal Graph Theory.

The present paper offers, in its first part, a unified approach for the derivation of families of inequalities for set functions which satisfy sub/supermodularity properties. It applies this approach for the derivation of information inequalities with Shannon information measures. Connections of the considered approach to a generalized version of Shearer's lemma, and other related results in the literature are considered. Some of the derived information inequalities are new, and also known results (such as a generalized version of Han's inequality) are reproduced in a simple and unified way. In its second part, this paper applies the generalized Han's inequality to analyze a problem in extremal graph theory. This problem is motivated and analyzed from the perspective of information theory, and the analysis leads to generalized and refined bounds. The two parts of this paper are meant to be independently accessible to the reader.

Research on the Shearer Positioning Method Based on the MEMS Inertial Sensors/Odometer Integrated Navigation System and RTS Smoother.

The shearer positioning method with an inertial measurement unit and the odometer is feasible in the longwall coal-mining process. However, the positioning accuracy will continue to decrease, especially for the micro-electromechanical inertial measurement unit (MIMU). In order to further improve the positioning accuracy of the shearer without adding other external sensors, the positioning method of the Rauch-Tung-Striebel (RTS) smoother-aided MIMU and odometer is proposed. A Kalman filter (KF) with the velocity and position measurements, which are provided by the odometer and closing path optimal estimation model (CPOEM), respectively, is established. The observability analysis is discussed to study the possible conditions under which the error states of KF can be estimated. A RTS smoother with the above-mentioned KF as the forward filter is built. Finally, the experiments of simulating the movement of the shearer through a mobile carrier were carried out, with a longitudinal movement distance of 44.6 m and a lateral advance distance of 1.2 m. The results show that the proposed method can effectively improve the positioning accuracy. In addition, the odometer scale factor and mounting angles can be estimated in real time.

A Generalized Information-Theoretic Approach for Bounding the Number of Independent Sets in Bipartite Graphs.

This paper studies the problem of upper bounding the number of independent sets in a graph, expressed in terms of its degree distribution. For bipartite regular graphs, Kahn (2001) established a tight upper bound using an information-theoretic approach, and he also conjectured an upper bound for general graphs. His conjectured bound was recently proved by Sah et al. (2019), using different techniques not involving information theory. The main contribution of this work is the extension of Kahn's information-theoretic proof technique to handle irregular bipartite graphs. In particular, when the bipartite graph is regular on one side, but may be irregular on the other, the extended entropy-based proof technique yields the same bound as was conjectured by Kahn (2001) and proved by Sah et al. (2019).

A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis.

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

Research on the Strategy of Motion Constraint-Aided ZUPT for the SINS Positioning System of a Shearer.

The accurate measurement of position and orientation for shearers is a key technology in realizing an automated, fully-mechanized, coal mining face. Since Global Positioning System (GPS) signal cannot arrive at the coal mine underground, wireless sensor network positioning system cannot operate stably in the coal mine; thus a strap-down inertial navigation system (SINS) is used to measure the position and orientation of the shearer. Aiming at the problem of the SINS accumulative error, this paper proposes a positioning error correction method based on the motion constraint-aided SINS zero velocity updated (ZUPT) model. First of all, a stationary state detection model of the shearer is built with median filter based on the acceleration and angular rate measured by the SINS. Secondly, the motion of the shearer is analyzed using coal mining technology, then the motion constraint model of the shearer is established. In addition, the alternate action between the motion constraint model and the ZUPT model is analyzed at the process of movement and cessation of the shearer, respectively; hence, the motion constraint-aided SINS ZUPT model is built. Finally, by means of the experimental platform of the SINS for the shearer, the experimental results show that the maximum position error with the positioning model proposed in this paper is 1.6 m in 180 s, and increases by 92.0% and 88.1% compared with the single motion constraint model and single ZUPT model, respectively. It can then restrain the accumulative error of the SINS effectively.

Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network.

In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy.

Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm.

Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.

Multi-Sensor Data Fusion Identification for Shearer Cutting Conditions Based on Parallel Quasi-Newton Neural Networks and the Dempster-Shafer Theory.

In order to efficiently and accurately identify the cutting condition of a shearer, this paper proposed an intelligent multi-sensor data fusion identification method using the parallel quasi-Newton neural network (PQN-NN) and the Dempster-Shafer (DS) theory. The vibration acceleration signals and current signal of six cutting conditions were collected from a self-designed experimental system and some special state features were extracted from the intrinsic mode functions (IMFs) based on the ensemble empirical mode decomposition (EEMD). In the experiment, three classifiers were trained and tested by the selected features of the measured data, and the DS theory was used to combine the identification results of three single classifiers. Furthermore, some comparisons with other methods were carried out. The experimental results indicate that the proposed method performs with higher detection accuracy and credibility than the competing algorithms. Finally, an industrial application example in the fully mechanized coal mining face was demonstrated to specify the effect of the proposed system.