Monte Carlo Method-Based Behavioral Reliability Analysis of Fully-Mechanized Coal Mining Operators in Underground Noise Environment

Abstract

Sound is an important aspect in working environment, and underground strong noise environment imposes a serious impact on operators' physical and mental health and easily leads to people's unsafe behaviors, thus giving rise to accidents. How to quantitatively study operators' behavioral reliability is a study hotspot. To mitigate the noise impact on operators and reduce the occurrence rate of accidents due to human factors, the relational models between noisy working environment and human physiological indexes were established first through a laboratory simulation, and then a reliability integral model was obtained using performance function and limit state equation. Second, the established reliability integral model was numerically simulated based on Monte Carlo numerical simulation method to obtain its numerical solution, and operators' behavioral reliability values under different sound pressure levels (SPLs) on fully-mechanized coal mining face were calculated. The results show that the behavioral reliability of fully-mechanized coal mining operators is high with low accident occurrence rate under noise SPL of 50-70 dB. Under 70-90 dB, their behavioral reliability is 0.7092 with potential accident risks. The behavioral reliability is low when SPL is 90-110 dB, under which accidents may easily take place. This study manifests that operators' behavioral reliability analysis under underground noise environment based on Monte Carlo method is of certain feasibility. The conclusions have a certain guiding significance for relieving human physical and mental harms incurred by noise, improving human behavioral reliability, reducing human errors and guaranteeing safety production.

Keywords

• behavioral reliability
• fully-mechanized coal mining face
• Monte Carlo method
• noise
• physiological index