一种轴流通风机故障诊断方法  

A fault diagnosis method for axial flow fan

作　　者：胡韶华[1] 谷振宇[2] 金迪文 HU Shaohua;GU Zhenyu;JIN Diwen(School of Electrical Engineering,Chongqing Vocational Institute of Engineering,Chongqing 402260,China;School of Automation,Chongqing University,Chongqing 400030,China)

机构地区：[1]重庆工程职业技术学院电气工程学院,重庆402260 [2]重庆大学自动化学院,重庆400030

出　　处：《工矿自动化》2018年第5期58-63,共6页Journal Of Mine Automation

基　　金：重庆市教委科研项目(KJ1603205);重庆工程职业技术学院科研重点项目(KJA2015-01)

摘　　要：针对现有基于谱分析的轴流通风机故障诊断方法只将故障类型和频谱特征值进行简单关联而导致诊断效果较差的问题,提出了一种基于矢椭谱和隐Markov模型的轴流通风机故障诊断方法。该方法首先将轴流通风机同一截面内互相垂直的2个振动信号在时域上直接融合为复信号,并对该复信号进行快速Fourier变换,获得多个特征频率下振动信号的全谱幅值;然后用不同故障状态下振动信号的全谱幅值训练隐Markov模型;最后以实时振动信号的全谱幅值作为隐Markov模型输入量,采用Viterbi算法计算隐Markov模型输出的似然概率,根据最大似然概率对数判断故障类型,避免了将振动幅值和故障类型进行简单关联。试验结果表明,该方法的故障诊断正确率达90%以上。For poor diagnosis effect of existing fault diagnosis methods for axial flow fan based on spectrum analysis which correlated fault type with spectrum characteristic value simply,a fault diagnosis method for axial flow fan based on vector ellipsoid spectrum and hidden Markov model(HMM)was proposed.Firstly,two orthogonal vibration signals of axial flow fan in the same section are fused into a complex signal in time domain,and full-spectrum amplitudes of the vibration signals under multi characteristic frequencies are obtained by fast Fourier transform of the complex signal.Secondly,the full-spectrum amplitudes under different fault conditions are used to train HMM.Finally,full-spectrum amplitudes of real-time vibration signals are as input of HMM,and Viterbi algorithm is used to calculate likelihood probability outputted by HMM.Fault type is judged according to the maximum logarithm value of the likelihood probability,which avoids simple association between the vibration amplitude and fault type.The experimental result shows that correct rate of fault diagnosis of the method is above 90%.

关 键 词：轴流通风机 故障诊断 故障识别 振动信号 特征频率 矢椭谱 隐MARKOV模型 

分 类 号：TD635[矿业工程—矿山机电]