基于多学科技术的煤矿局部通风系统故障诊断方法

Fault Diagnosis Approach of Local Ventilation System in Coal Mines Based on Multidisciplinary Technology

为了减少煤矿局部通风系统故障发生的概率和防止瓦斯超出标准限制,把可靠性分析、粗糙集理论、遗传算法(GA)和智能决策支持系统(IDSS)相结合来建立和发展煤矿局部通风系统的故障诊断方法。该故障树模型的建立及其可靠性分析把获取故障的主要症状和故障诊断规则进行了分析和发展。最后,通过一个矿井实例对该模型系统进行开发和展示。结果表明,该方法不但能迅速准确地找到煤矿局部通风系统产生故障的原因,而且可以减少局部通风系统故障诊断的困难。

In order to reduce the probability of fault occurrence of local ventilation system in coal mine and prevent gas from exceeding the standard limit, an approach incorporating the reliability analysis, rough set theory, genetic algorithm （GA）, and intelligent decision support system （IDSS） are used to establish and develop a fault diagnosis system of local ventilation in coal mine. Fault tree model is established and its reliability analysis is performed. The algorithms and software of key fault symptom and fault diagnosis rule acquiring are also analyzed and developed. Finally, a prototype system is developed and demonstrated by a mine instance. The research results indicate that the proposed approach in this paper can accurately and quickly find the fault reason in a local ventilation system of coal mine and can reduce difficulty of the fanIt diagnosis of the local ventilation system.