A fault diagnosis method for axial flow fa
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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。Abstract: 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%.
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