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.

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.

Mine hoists are vital for efficiently transporting materials and personnel between surface and underground mining operations, making their safety and reliability paramount. However, conventional monitoring approaches, which rely on distributed sensors or video surveillance, are limited in their ability to capture the global dynamics of the system, leading to suboptimal decision-making and potential safety risks. This research introduces the integration of digital twin technology into the monitoring and management of mine hoists to address these challenges. This study uses Unity-based modeling to create a virtual system capable of real-time synchronization with its physical counterpart. The digital twin simulates dynamic load changes, predicts potential faults, and optimizes operational parameters through intelligent algorithms. A robust monitoring framework was developed, consisting of a multi-layered architecture that integrates physical systems, real-time data collection, and virtual simulations. Field tests on a multi-rope friction hoist verified the system’s performance, with results demonstrating improved stability, accuracy, and predictive capabilities. A health evaluation model further enhances safety by categorizing the hoist’s operational state into health levels such as ‘healthy,’ ‘sub-healthy,’ ‘warning,’ and ‘fault.’ This model identifies critical risks, such as wire rope tension anomalies, and provides early warnings, ensuring timely interventions.