In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

中文翻译：

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一种改进的复合多元多尺度波动色散熵及其在旋转机械多元信号中的应用

在旋转机械故障监测中，轴承和齿轮在复杂运行环境下的振动信号平稳性差，噪声大。如何准确、高效地识别各种故障类别是旋转故障诊断的一大挑战。现有的方法大多只分析单通道振动信号，没有综合考虑多通道振动信号。为此，本文提出了一种提取不同尺度因子的多通道信号识别信息的方法，即精炼复合多元多尺度波动色散熵(RCMMFDE)，精炼复合分析保证了识别的稳定性。仿真结果表明，该方法对信号长度敏感度低，抗噪声能力强。同时，结合联合互信息最大化（JMIM）和支持向量机（SVM），提出了RCMMFDE-JMIM-SVM故障诊断方法。该方法利用RCMMFDE提取旋转机械多个振动信号的状态特征，再利用JMIM方法提取敏感特征。最后，通过支持向量机对旋转机械的不同状态进行分类。通过复合齿轮故障数据集和轴承故障数据集验证了该方法的有效性。该方法的诊断准确率分别为99.25%和100.00%。实验结果表明，RCMMFDE-JMIM-SVM可以有效识别多个信号。提出了RCMMFDE-JMIM-SVM故障诊断方法。该方法利用RCMMFDE提取旋转机械多个振动信号的状态特征，再利用JMIM方法提取敏感特征。最后，通过支持向量机对旋转机械的不同状态进行分类。通过复合齿轮故障数据集和轴承故障数据集验证了该方法的有效性。该方法的诊断准确率分别为99.25%和100.00%。实验结果表明，RCMMFDE-JMIM-SVM可以有效识别多个信号。提出了RCMMFDE-JMIM-SVM故障诊断方法。该方法利用RCMMFDE提取旋转机械多个振动信号的状态特征，再利用JMIM方法提取敏感特征。最后，通过支持向量机对旋转机械的不同状态进行分类。通过复合齿轮故障数据集和轴承故障数据集验证了该方法的有效性。该方法的诊断准确率分别为99.25%和100.00%。实验结果表明，RCMMFDE-JMIM-SVM可以有效识别多个信号。通过复合齿轮故障数据集和轴承故障数据集验证了该方法的有效性。该方法的诊断准确率分别为99.25%和100.00%。实验结果表明，RCMMFDE-JMIM-SVM可以有效识别多个信号。通过复合齿轮故障数据集和轴承故障数据集验证了该方法的有效性。该方法的诊断准确率分别为99.25%和100.00%。实验结果表明，RCMMFDE-JMIM-SVM可以有效识别多个信号。