Achieving precise stopping of electric locomotives is crucial for the realization of intelligent and unmanned auxiliary transportation systems. Presently, human drivers play a central role in ensuring accurate stopping, presenting obstacles to automation and cargo location precision, especially within the coal mining sector. This article centers on achieving the precise stopping of electric locomotives under various conditions through the utilization of permanent magnet synchronous motor-driven locomotives. This approach introduces a novel stopping control method that integrates a fuzzy proportional–integral–derivative (F-PID) controller with a vector control model for permanent magnet synchronous motors (PMSM). Subsequently, we develop the F-PID controller using the PMSM technique, incorporating new fuzzy rules for each subsystem to enhance control accuracy and efficiency. Finally, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed to validate the effectiveness of the proposed control method. The results show that the method consistently achieves precise stopping under diverse working conditions, with an error of less than 0.3 m, confirming its robustness and reliability.

Ensuring precise electric locomotive stopping is pivotal for achieving intelligent and unmanned auxiliary transportation systems. Currently, human drivers' expertise plays a central role in the stopping process, posing challenges for automation and cargo location accuracy, particularly in coal mining. This paper focuses on achieving accurate electric locomotive stopping under diverse conditions by utilizing permanent magnet synchronous motor-driven locomotives. This technique introduces a novel stopping control method that combines a fuzzy Proportional-Integral-Derivative (F-PID) controller and a vector control model for permanent magnet synchronous motors (PMSM). After this, we design the F-PID using the PMSM technique based on the new fuzzy rules for each sub-system. In the end, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed, which demonstrate the effectiveness of the proposed control method. From the simulation results, it confirming that the ability to achieve precise stopping under various working conditions.

As stringent emission regulations and safety standards for explosion-proof diesel engines become more critical, the demand for efficient exhaust cooling systems has increased. Traditional cooling systems, typically relying on cooling and purification water tanks, have limitations in terms of safety, performance, and emissions control. To address these challenges, a novel dry exhaust gas cooling system was developed, incorporating a heat exchanger and exhaust dilution cooling device, replacing the conventional water-based cooling systems. This study explores the performance of the dry exhaust gas cooling system through a series of experiments including explosion-proof testing of the exhaust system, whole machine explosion-proof testing, exhaust temperature measurements, surface temperature evaluations, and exhaust gas composition analysis. The system’s performance was compared to both wet and combined dry + wet exhaust gas cooling systems. Results showed that the dry exhaust cooling system maintained its explosion-proof integrity during all tests, with the highest exhaust temperature at 68.5 °C and a surface temperature of 130.8 °C—both of which comply with safety standards. Notably, the dry exhaust system also demonstrated improved power output and reduced fuel consumption by over 4% compared to the other systems. Furthermore, it significantly lowered harmful exhaust emissions, reducing CO, HC, NOX, and CO₂ levels by 55%, 71%, 68%, and 82%, respectively, when compared to the wet exhaust cooling system. In comparison to the dry + wet system, these reductions were even more pronounced—63%, 75%, 66%, and 94%, respectively. The findings suggest that the dry exhaust gas cooling system offers a safer, more efficient, and environmentally friendly alternative to conventional exhaust cooling systems in explosion-proof diesel engines.

In order to study the lateral vibration characteristics of the vertical wire rope of the winding hoisting system, a virtual prototype model including drum, wire rope, head sheaves and guides is established by ADAMS/Cable module. Dynamic simulation of winding hoisting process is carried out by external solution with ADAMS/Solver simulation control language. The validity of the model is verified with a winding hoisting test-bed, the dynamic behavior of the hoisting system under variable speed conditions is analyzed, and the lateral vibration law and the influence on the longitudinal vibration of the suspension rope under the speed change are obtained.

Accurate fault diagnosis of mining hoisting head sheave systems is critical for ensuring operational safety in harsh underground environments. This study proposes a multi-source fault diagnosis framework that fuses vibration and acoustic information using a Convolutional Neural Network and Random Forest (CNN-RF). To support mechanism understanding and validate the experimental platform, finite element and multi-body dynamics simulations (ANSYS/ADAMS) are employed for physical verification and fault signature analysis, while the CNN-RF model is trained and tested exclusively using experimentally acquired vibration and acoustic data. For feature construction, vibration signals are transformed into time–frequency representations (including STFT, CWT, and generalized S-Transform (GST)), and acoustic signals are characterized using Mel-Frequency Cepstral Coefficients (MFCCs). Experimental results demonstrate that vibration–acoustic fusion improves diagnostic performance compared with single-modality baselines; the best performance is achieved by GST+MFCC with the proposed CNN-RF classifier, reaching an accuracy of 98.96%. Future work will conduct cross-condition validation under varying speeds and loads and investigate missing-modality robustness to further assess generalization and deployment reliability.

By analysing the source of vibration of the main rope, and deriving the excitation function of the head sheave and the cage guides, a physical model of the vibration of the main rope is established. The generalized Hamilton principle is used to establish the transverse and longitudinal coupled vibration equation of the main rope. Considering the influence of rigid cage guides and head sheaves excitation on the main rope vibration, the main rope vibration control equation is discretized and solved based on the Galerkin truncation method to obtain the main rope transverse vibration displacement curve. At the same time, the machine vision image matching method is used to realize the measurement of the transverse vibration of the vertical rope, and the pixel coordinates of the target are converted into actual physical coordinates through coordinate conversion, thereby calculating the transverse vibration displacement of the vertical rope. Finally, the results of comparison experiments show that the above matching algorithm has strong practicability for measuring the transverse vibration of the hoisting main rope.