Liu Hanzhong, Qin Chaoxuan, Liu Ming
School of Automation, Nanjing Institute of Technology, Nanjing 211167, China.
Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China.
Sensors (Basel). 2019 Jul 26;19(15):3300. doi: 10.3390/s19153300.
For compound fault detection of high-speed rail vibration signals, which presents a difficult problem, an early fault diagnosis method of an improved empirical mode decomposition (EMD) algorithm based on quartic C Hermite interpolation is presented. First, the quartic C Hermite interpolation improved EMD algorithm is used to decompose the original signal, and the intrinsic mode function (IMF) components are obtained. Second, singular value decomposition for the IMF components is performed to determine the principal components of the signal. Then, the signal is reconstructed and the kurtosis and approximate entropy values are calculated as the eigenvalues of fault diagnosis. Finally, fault diagnosis is executed based on the support vector machine (SVM). This method is applied for the fault diagnosis of high-speed rails, and experimental results show that the method presented in this paper is superior to the traditional EMD algorithm and greatly improves the accuracy of fault diagnosis.
针对高速列车振动信号复合故障检测这一难题，提出了一种基于四次C Hermite插值的改进经验模态分解（EMD）算法的早期故障诊断方法。首先，利用四次C Hermite插值改进的EMD算法对原始信号进行分解，得到本征模态函数（IMF）分量。其次，对IMF分量进行奇异值分解以确定信号的主分量。然后，对信号进行重构，并计算峭度和近似熵值作为故障诊断的特征值。最后，基于支持向量机（SVM）进行故障诊断。该方法应用于高速列车的故障诊断，实验结果表明本文提出的方法优于传统的EMD算法，大大提高了故障诊断的准确率。
