Software Institute, Dalian Jiaotong University, Dalian 116028, China.
Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Yantai 264005, China.
Sensors (Basel). 2018 Oct 3;18(10):3323. doi: 10.3390/s18103323.
Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery.
经验模态分解（EMD）是一种自适应信号分解方法，其主要缺点是傅里叶分段强烈依赖于傅里叶谱的幅度的局部最大值。为了实现电机轴承的故障诊断，提出了一种基于最大-最小长度曲线法的改进经验小波变换（MSCEWT）。最大-最小长度曲线法将原始振动信号频谱转换到尺度空间，以获得一组最小长度曲线，并在最小长度曲线值集合中找到最大长度曲线值，以获得频谱分解间隔的数量。MSCEWT 方法用于将振动信号分解为一系列固有模态函数（IMF），并通过希尔伯特变换对其进行处理。然后通过功率谱提取每个分量的频率，并与电机轴承故障特征频率的理论值进行比较，以确定并获得故障诊断结果。为了验证 MSCEWT 方法在故障诊断中的有效性，选择了实际的电机轴承振动信号，并选择了经验模态分解（EMD）和集合经验模态分解（EEMD）方法进行对比分析。结果表明，最大-最小长度曲线法可以增强 EWT 方法，MSCEWT 方法可以解决傅里叶谱分段的缺点，并可以有效地分解轴承振动信号，获得比 EMD 和 EEMD 方法更少的固有模态函数（IMF）分量。它可以有效地提取电机轴承的故障特征频率，实现故障诊断。因此，该研究为旋转机械的故障诊断提供了一种新方法。
