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滚动轴承故障振动信号是典型的调幅信号,而谱相关密度分析对调幅信号具有解调功能,它可以有效地提取出滚动轴承信号的故障特征,结合连续隐马尔可夫模型(Hidden Markov Model,HMM)所具有的强大时序模式分类能力,提出了基于谱相关密度-连续HMM的滚动轴承故障诊断方法。该方法首先利用谱相关密度函数在循环频率处进行切片分析,提取滚动轴承故障振动信号的特征,构成特征向量序列;然后将此序列输入到连续HMM中进行训练,得到各类对应故障的模型,最后利用训练好的模型进行滚动轴承的故障诊断。试验结果验证了该方法的可行性和有效性。
The vibration signal of the rolling bearing is a typical amplitude modulation signal, while the spectral density correlation analysis has the demodulation function to the amplitude modulation signal. It can effectively extract the fault characteristics of the rolling bearing signal. Combining the Hidden Markov Model (HMM) Has a strong ability of sequential mode classification, a method of fault diagnosis of rolling bearing based on spectral density-continuous HMM is proposed. In the method, the spectral correlation density function is used to segment the fault frequency at the cycle frequency to extract the characteristics of the fault vibration signal of the rolling bearing to form a sequence of eigenvectors. Then, the sequence is input into the continuous HMM for training, and the corresponding fault models are obtained. Finally, Use the trained model to diagnose the fault of rolling bearing. The experimental results verify the feasibility and effectiveness of this method.