基于改进模糊推理理论的矿井主通风机故障诊断

Fault Diagnosis of Mine Main Ventilator Based on Improved Fuzzy Reasoning Theory

针对矿井主通风机故障类型多、故障间的关联耦合度高、检测困难等问题,提出一种基于改进模糊推理理论的矿井主通风机故障检测模型。首先利用模糊推理理论将构建的主通风机故障规则库转换为故障关系模糊矩阵,然后引入最大隶属度函数原则,通过模糊算子计算、合并等过程最终找出故障发生的置信度最高的故障原因。仿真结果表明,该模型能够达到高效、快速排除故障的目的,对于解决当前矿井主通风机系统的故障检测或预判问题具有非常重要的意义。

In view of the problems of the mine main ventilator, such as many types of faults, high correlation coupling between faults, and difficult detection, a fault detection model of mine main ventilator based on improved fuzzy reasoning theory is proposed. Firstly, the fuzzy inference theory is used to transform the fault rule base of the main ventilator into the fuzzy fault relation matrix. Then, the maximum membership function principle is introduced and the fault reason with the highest confidence degree is found out through the calculation and combination of fuzzy operators. The simulation results show that the model can achieve the purpose of efficient and rapid troubleshooting, which is of great significance to solve the problem of fault detection or prediction of the current mine main ventilation system.