This paper proposes a path correction scheduling strategy for the underground mining boom roadheader by ably combining a back propagation (BP) neural network and state estimation. First, a pose deviation-based tracking model is designed for the roadheader, and it is then further studied and optimized by incorporating the benefits of BP neural networks into the model adaptation. Considering the fact that there is skidding between tracks on the ground and errors during the instant pose detection of the roadheader underground, singular value decomposition (SVD)–Unscented Kalman filtering is applied to estimate the real pose deviation, based on the summarized distribution regularities of the track skidding ratios and the pose detection errors, instead of complicated analysis mechanisms. The BP neural network and states estimation are well combined in structure, enabling this scheduling strategy to update the control law and revise the control instruction simultaneously in the procedure. The proposed path tracking model for the roadheader is simple and clear, without adding extra devices or massive algorithms, which is attractive in terms of industrial use. The path tracking simulations show that this proposed strategy achieves path tracking well in different scenarios and is of high adaptability when facing complex trajectory while still giving stable control instructions for the roadheader.

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.

In this paper, based on the multimode interference structure fiber and the sensitive advantages of a zeolitic imidazolate framework-8/Polydimethylsiloxane (ZIF-8/PDMS)-sensitive film in methane detection, a methane sensor based on an interferometer induced by multimode interference is designed and built with the aid of modeling. The methane-sensitive single mode fiber (MS-SMF) is obtained by coating a ZIF-8/PDMS-sensitive film around the cladding of a thin-diameter SMF. The change in methane concentration leads to a change in the cladding mode of the MS-SMF, which causes a change in interference spectrum and realizes methane concentration sensing. The factors affecting the sensitivity of the methane sensor are analyzed. Methane sensors with various parameters are fabricated and tested on a methane sensor platform for performance estimation at methane concentrations of 0–4%. The experimental results show that the sensitivity of the sensor to methane reaches 2.364 nm/% when the length of the MS-SMF is 42 mm, the thickness of the sensitive film is 1.8 µm, and the diameter of the MS-SMF is 58 µm. The limit of detection is about 338 ppm. The average response time is 30 s and the recovery time is 45 s. The temperature sensitivity of the methane sensor is approximately 0.026 nm/°C. The experimental results verify the correctness of the methane sensor model. This study provides a new design idea for optical methane sensors, showing great application potential in the field of methane detection.

Trajectory planning and tracking control algorithm based on a position and orientation deviation model are proposed to achieve path correction for the mining boom road‐header working underground. The proposed strategy is assessed to be feasible and potentially practicable by simulations, from which the following statements are summarized. Firstly, trajectory planning is necessary since different trajectories correspond to different scenarios about undesirable excavation space, slipping level, and power consumption. Secondly, using the proposed tracking control algorithm, the road‐header is guided back onto the expected path in limited adjusting steps, with smoothly varying rotation speeds of the driving wheels and regularly reducing pose errors. Lastly, it shows that by implying the SVD‐unscented Kalman filtering in the tracking control, the adverse impacts of process and measurement noises are appeased obviously. This research provides an advisable modeling and valuable simulation for the road‐header to achieve robotic operation underground.

The safe and stable operation of roadheader is of great significance to the efficient and rapid production of a coal mine. Health diagnosis based on vibration signals has been studied in bearings and motors. Complex geological conditions and bad working environment lead to the characteristics of nonlinear and time‐varying vibration signals of a roadheader. In this paper, a health state analysis method based on reference manifold (RM) learning and improved K‐means clustering analysis was proposed; the method was verified by using the real‐time collected roadheader cutting reducer fault signal. Firstly, the comparison signal and analysis signal were extracted from the actual collected vibration data of the roadheader, and the referential analysis samples were constructed through time domain and wavelet packet energy analysis. Then, the characteristic structure of the low‐dimensional space of the referential analysis samples is obtained by Locally Linear Embedding (LLE), which is a method of manifold learning. Through the improved K‐means clustering analysis method, the low‐dimensional structure parameters were analyzed and the clustering effect index was obtained, which was used as the health evaluation index (HEI). Finally, the normal distribution model of the health evaluation index is established, and the confidence interval of the health evaluation index is determined, so as to realize the health state analysis of the roadheader and realize the fault warning function. Through the analysis of data of three sensors, the results show that the roadheader failed on the 15th day, which is consistent with the actual working condition. Through practical analysis, the effectiveness of the method was verified and provided a kind of fault analysis idea and method for equipment working under complex working conditions and the theoretical basis for fault type analysis.