Abstract
The automatic ventilation door in mining operations is a crucial component for ensuring production safety and maintaining ventilation system stability. However, the primary power element of this equipment-the cylinder-often lacks effective monitoring, which can compromise operational reliability. To address this gap, this study proposes a Weibull life prediction method, integrating Bayesian inference and Monte Carlo simulation, aiming at anticipating changes in cylinder reliability. This proactive approach supports timely maintenance to prevent. Given the unknown shape and scale parameters of the Weibull distribution, Bayesian methodology is applied, alongside accelerated life testing principles, to analyze the life characteristics of cylinder. By deriving the posterior distribution function of Weibull parameters, Monte Carlo simulation is employed to estimate these parameters across various operational conditions. This method reveals how life characteristics relate to environmental factors such as temperature. Following the constant-failure-mechanism assumption used in accelerated life testing, the characteristic parameters of cylinder characteristic parameters under standard operating conditions are predicted. Results show that this method is effective for life prediction using truncated small-sample data, overcoming the limitations of conventional approaches. Its applicability is proven in the life assessment of automatic ventilation doors, offering a robust tool for reliability. A reliability evaluation system for mine emergency control equipment is developed. This system provides real-time assessments and visualizations of equipment reliability, enhancing maintenance and management practices essential for mining operations.