Hai Jiang

The working environment at coal mining faces is harsh, leading to high failure rates and significant maintenance issues with roadheaders. This study explores multi-layer dimensionality reduction of vibration signal features in complex environments to enhance the differentiation of different operational states of a roadheader, thereby achieving fault recognition of key components. Concurrently, reducing dimensionality in manifold spaces positively influences operational state differentiation. Therefore, this paper integrates manifold learning to conduct multi-sensor and multi-layer data mining to enhance the differential phenotypes between faults of key components of the roadheader. Initially, we constructed multiple status-reference sample sets for each sensor individually, forming multiple manifolds at different spatial points, and utilizing locality-preserving projections (LPP) to extract low-dimensional manifold features. Further fusion of low-dimensional features from multiple sensors was used to elevate samples, constructing an enhanced spatial pseudo-manifold. Finally, we used LPP to re-reduce the enhanced sensitive feature set from multiple vibration sensors, establishing a dual-layer sensitive feature enhancement learning model. Conducting fault recognition analysis on experimental vibration signals, using k-nearest neighbors (KNN) to classify the enhanced feature set, we achieved a recognition success rate of 98.75% for samples, proving the method’s feasibility in fault recognition under complex loads.

This paper introduces a control method tailored for the speed regulation of monorail cranes in coal mines. Initially, an analysis of the structure and load conditions of the monorail crane drive components is conducted to calculate the traction force, clamping force, and target travel speed across varying operational scenarios. Subsequently, the hydraulic system schematic of the monorail crane is analyzed to develop a mathematical model for speed control, enabling the assessment of system stability using transfer functions. A simulation model of the monorail crane speed control loop is then created in AMESim, where fuzzy adaptive PID controllers and MPC controllers are optimized in a collaborative simulation with Simulink. Experimental findings reveal that in a single acceleration condition, both controllers demonstrate superior dynamic response compared to a traditional PID controller, with the MPC controller exhibiting an overshoot of merely 8.9%. In speed variation conditions, the MPC controller achieved a settling time in the range of 0.26–0.3 s. Notably, the MPC controller displays a maximum overshoot of 11%, substantially enhancing the dynamic response performance of speed regulation in monorail cranes.

Roadheader is important large equipment in coal mining. The roadheader has a higher failure rate due to its harsh working environment and high working intensity. In this paper, we proposed a fault diagnosis method based on reference manifold (RM) learning by using the vibration signals of roadheader in the actual production process. First, health and fault vibration signals were extracted from a large number of field data. The abovementioned signals were analyzed by time domain and wavelet packet energy analysis and got the characteristic parameters of the signal which can form the characteristic parameter sets. RM method can reduce the dimension of the characteristic parameters, and the projection of different characteristic parameters was obtained. Finally, the health parameters and fault parameters of different characteristic parameters were segmented by linear discriminant analysis (LDA). It could get the different segment area range of characteristic parameters for health signals and fault signals. This method provides a set of fault analysis ideas and methods for equipment working under complex working conditions and improves the theoretical basis for fault type analysis.

Underground coal mines belong to the GNSS-denied environment, and the Strapdown Inertial Navigation System (SINS) has a significant advantage in the precise positioning of equipment in this environment because of its operation without requiring interaction with external information and strong anti-interference capabilities. Nonetheless, the vibrations of the installation platform adversely affect the positioning accuracy of SINS. This article focuses on the monorail crane in coal mines as the subject of research, developing a dynamic model for the motion unit consisting of the “track + drive unit + driver’s cab”, while analyzing the relationship between track roughness conditions and the vibration excitation of this unit. Subsequently, utilizing the dynamic model, the study calculated the angular and linear vibration characteristics and formulated models to address coning error and sculling error specific to the SINS in this vibration condition. Lastly, by employing a multi-sample compensation algorithm, this article compensated for positioning errors in the SINS caused by track roughness-induced vibrations during uniform straight-line motion of the motion unit, thus achieving optimal positioning information for the monorail crane. The simulation results demonstrated that employing a four-sample compensation algorithm reduces the coning error in SINS positioning calculations by a minimum of 50% and decreases the sculling error by at least 31%, satisfying the positioning accuracy requirements for precise parking of the monorail crane during the transportation phase, while establishing the foundation for autonomous precise positioning and integrated navigation of underground track transport equipment in coal mines.

The tunneling work belongs to the group operation of semi-closed space, and the work is difficult with a high risk coefficient. It is an urgent requirement of coal mining to achieve unmanned and intelligent tunneling work. The path rectification planning of roadheaders is a necessary step before roadway cutting. In the traditional dynamic modeling analysis of roadhead tracks, problems such as compaction resistance, bulldozing resistance, steering resistance, tunnel dip angle, ditching, and obstacle-crossing capacity are not considered. In order to approximate the kinematic and dynamic parameters of a roadheader’s deviation correction under actual working conditions, this paper establishes kinematic and dynamic models of a roadheader’s path rectification at low speeds and under complex working conditions, and calculates the obstacle-crossing ability of roadheaders in the course of path rectification by modes based on roadway conditions, crawler resistance, and driving performance of the roadheader. Field experiments were carried out to verify the effectiveness of the dynamic model. The dynamic roadheader model was used in combination with actual working conditions of roadways in order to establish a roadway grid model. The grid model was simplified using rectifying influence degree and distance cost. The roadheader dynamic model and grid model were then used to propose a path rectification planning and tracking algorithm based on particle swarm optimization of the actual roadway conditions and roadheader driving performance. Finally, the effectiveness and superiority of the algorithm were verified using MATLAB simulation. The algorithm can provide strong technical guarantee for the intelligence of roadheader and unmanned mining. The results presented here can provide theoretical and technical support for the structural optimization and intelligent travel control of roadheaders.

The strapdown inertial navigation system can provide the navigation information for the boom-type roadheader in the unmanned roadway tunneling working face of the coal mine. However, the complex vibration caused by the cutting process of the boom-type roadheader may result in significant errors of its attitude and position measured by the strapdown inertial navigation system. Thus, an error compensation method based on the vibration characteristics of the roadheader is proposed in this paper. In order to further analyze the angular and linear vibration of the fuselage, as the main vibration sources of the roadheader, the dynamic model of the roadheader is formulated based on the cutting load. Following that, multiple sub-samples compensation algorithms for the coning and sculling errors are constructed. Simulation experiments were carried out under different subsample compensation algorithms, different coal and rock characteristics, and different types of roadheader. The experimental results show that the proposed error compensation algorithm can eliminate the effect of the angular and linear vibration on the measurement accuracy. The coning and sculling error of the strapdown inertial navigation system can reduce at least 52.21% and 42.89%, respectively. Finally, a strapdown inertial navigation error compensation accuracy experiment system is built, and the validity and superiority of the method proposed in this paper are verified through calculation and analysis of the data collected on the actual tunneling work face.