主通风机机械故障智能诊断研究

Research on Intelligent Diagnosis of Mechanical Faults of Main Ventilator

为了确保煤矿主通风机安全运行和机械故障智能诊断的精确性,提出了基于EEMD算法、能量 熵和SOM神经网络的主通风机机械故障智能诊断方法。首先采用EEMD算法对主通风机振动信号进行分解 处理并得出IMF分量,然后通过能量熵方法对IMF分量进行特征向量提取,用于后续SOM神经网络的故障诊 断,最后在MATLAB上实现故障类型诊断栅格结构图。测试结果证明,该方法具有一定的可行性,并提高了 主通风机机械故障智能诊断的精确度。

In order to ensure the safe operation of main ventilator in coal mine and the accuracy of intelligent fault diagnosis,an intelligent fault diagnosis method of main ventilator based on EEMD algorithm,energy entropy and SOM neural network was proposed.Firstly,the vibration signal of main ventilator is decomposed and processed by EEMD algorithm,and the IMF component is obtained.Then,the feature vector of IMF component is extracted by energy entropy method,which is used for fault diagnosis of subsequent SOM neural network.Finally,the fault type diagnosis grid structure is realized in MATLAB.The test results show that the method is feasible and improves the accuracy of intelligent diagnosis of mechanical faults of main ventilator.