Li Hua, Liu Tao, Wu Xing, Chen Qing
Lab. of Vib. & Noise under Ministry of Education of Yunnan Province, Kunming University of Science and Technology, Kunming 650500, China.
ISA Trans. 2019 May;88:170-185. doi: 10.1016/j.isatra.2018.12.002. Epub 2018 Dec 5.
Ensemble empirical mode decomposition (EEMD) is widely used in condition monitoring of modern machine for its unique advantages. However, when the signal-to-noise ratio is low, the de-noising function of it is often not ideal. Thus, a new fault feature extraction method for rolling bearing combining EEMD and improved frequency band entropy (IFBE) is proposed, i.e., EEMD-IFBE. According to the problem of multiple intrinsic mode functions (IMFs) generated by EEMD, how to select the sensitive IMF(s) that can better reflect fault characteristics, a novel method based on FBE for sensitive IMF is proposed. In addition, since the bandwidth parameter is set empirically when the band-pass filter is designed based on the original FBE, a novel bandwidth parameter optimization method based on the principle of maximum envelope kurtosis is proposed. First, the original vibration signal is subjected to EEMD to obtain a series of IMFs; Then, the FBE values are obtained for the original signal and each IMF component, and the bandwidth of the band-pass filter (empirically) is designed as the characteristic frequency band at the minimum entropy value, and the affiliation between the characteristic frequency band of each IMF and the characteristic frequency band of the original signal is compared, and then selecting the sensitive IMF(s) that reflects the characteristics of the fault; Third, due to the influence of background noise, it is difficult to accurately obtain the fault frequency from the selected IMF(s). Therefore, the band-pass filter designed based on FBE is used, and the bandwidth parameter is optimized based on the principle of envelope kurtosis maximum, and then the selected sensitive IMF is band-pass filtered. Finally, the envelope power spectrum analysis is performed on the filtered signal to extract the fault characteristic frequency, and then the fault diagnosis of the bearing is realized. The method is successfully applied to simulated data and actual data of rolling bearing, which can accurately diagnose fault characteristics of bearing and prove the effectiveness and advantages of the method.
总体经验模态分解（EEMD）因其独特优势在现代机器状态监测中得到广泛应用。然而，当信噪比很低时，其去噪功能往往不理想。因此，提出了一种将EEMD与改进的频带熵（IFBE）相结合的滚动轴承故障特征提取新方法，即EEMD - IFBE。针对EEMD产生多个固有模态函数（IMF）时如何选择能更好反映故障特征的敏感IMF这一问题，提出了一种基于频带熵的敏感IMF选择新方法。此外，由于基于原始频带熵设计带通滤波器时带宽参数是凭经验设置的，提出了一种基于最大包络峭度原理的带宽参数优化新方法。首先，对原始振动信号进行EEMD得到一系列IMF；然后，求出原始信号及各IMF分量的频带熵值，并凭经验将带通滤波器带宽设计为最小熵值处的特征频带，比较各IMF的特征频带与原始信号特征频带的隶属关系，进而选择反映故障特征的敏感IMF；第三，由于背景噪声的影响，从所选的IMF中难以准确获取故障频率。因此，使用基于频带熵设计的带通滤波器，并基于包络峭度最大原理对带宽参数进行优化，然后对所选敏感IMF进行带通滤波。最后，对滤波后的信号进行包络功率谱分析以提取故障特征频率，进而实现轴承的故障诊断。该方法成功应用于滚动轴承的模拟数据和实际数据，能够准确诊断轴承的故障特征，证明了该方法的有效性和优势。
