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The primary function of a mine hoist is the transportation of personnel and equipment, serving as a crucial link between underground and surface systems. The proper functioning of key components such as work braking and safety braking is essential for ensuring the safety of both personnel and equipment, thereby playing a critical role in the safe operation of coal mines. As coal mining operations extend to greater depths, they introduce heightened challenges for safe transportation, compounded by increased equipment loss. Consequently, there is a pressing need to enhance safety protocols to safeguard personnel and materials. Traditional maintenance and repair methods, characterized by routine equipment inspections and scheduled downtime, often fall short in addressing emerging issues promptly, leading to production delays and heightened risks for maintenance personnel. This underscores the necessity of adopting predictive maintenance strategies, leveraging digital twin models to anticipate and prevent potential faults in mine hoists. In summary, the implementation of predictive maintenance techniques grounded in digital twin technology represents a proactive and scientifically rigorous approach to ensuring the continued safe operation of mine hoists amidst the evolving challenges of deepening coal mining operations. In this study, we propose the integration of a CNN-LSTM algorithm within a digital twin framework for predicting faults in mine hoist braking systems. Utilizing software such as AMESim 2019 and MATLAB 2016b, we conduct joint simulations of the hoist braking digital twin system. Subsequently, leveraging the simulation model, we establish a fault diagnosis platform for the hoist braking system. Finally, employing the CNN-LSTM network model, we forecast failures in the mine hoist braking system. Experimental findings demonstrate the effectiveness of our proposed algorithm, achieving a prediction accuracy of 95.35%. Comparative analysis against alternative algorithms confirms the superior performance of our approach. [ABSTRACT FROM AUTHOR]

As the manufacturing industry evolves, the significance of control valve positioners in chemical production escalates. The flapper–nozzle system, the heart of control valve positioners, directly influences the linearity of system control. Presently, studies on the flapper–nozzle system primarily focus on dynamic system modeling and computational fluid dynamics simulations. However, traditional flapper–nozzle mechanisms often fail to achieve linear control objectives. This paper proposes a novel negative-pressure nozzle structure to tackle this issue, combining computational fluid dynamics and experimental methods, and considering gas compressibility during high-speed flow. Both simulation and experimental results suggest that the new structure improves the supply air pressure and broadens the linear pressure output range of the system, showing significant potential for practical applications. [ABSTRACT FROM AUTHOR]