Yuan Qiang, Lv Mingchen, Zhou Ruiping, Liu Hong, Liang Chongkun, Cheng Lijiao
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, China.
School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China.
Entropy (Basel). 2023 Jul 12;25(7):1049. doi: 10.3390/e25071049.
The study focuses on the fault signals of rolling bearings, which are characterized by nonlinearity, periodic impact, and low signal-to-noise ratio. The advantages of entropy calculation in analyzing time series data were combined with the high calculation accuracy of Multiscale Fuzzy Entropy (MFE) and the strong noise resistance of Multiscale Permutation Entropy (MPE), a multivariate coarse-grained form was introduced, and the coarse-grained process was improved. The Composite Multivariate Multiscale Permutation Fuzzy Entropy (CMvMPFE) method was proposed to solve the problems of low accuracy, large entropy perturbation, and information loss in the calculation process of fault feature parameters. This method extracts the fault characteristics of rolling bearings more comprehensively and accurately. The CMvMPFE method was used to calculate the entropy value of the rolling bearing experimental fault data, and Support Vector Machine (SVM) was used for fault diagnosis analysis. By comparing with MPFE, the Composite Multiscale Permutation Fuzzy Entropy (CMPFE) and the Multivariate Multiscale Permutation Fuzzy Entropy (MvMPFE) methods, the results of the calculations show that the CMvMPFE method can extract rolling bearing fault characteristics more comprehensively and accurately, and it also has good robustness.
该研究聚焦于滚动轴承的故障信号，其具有非线性、周期性冲击以及低信噪比的特点。将熵计算在分析时间序列数据方面的优势与多尺度模糊熵（MFE）的高计算精度和多尺度排列熵（MPE）的强抗噪性相结合，引入了一种多元粗粒化形式，并对粗粒化过程进行了改进。提出了复合多元多尺度排列模糊熵（CMvMPFE）方法，以解决故障特征参数计算过程中精度低、熵扰动大以及信息丢失等问题。该方法能更全面、准确地提取滚动轴承的故障特征。利用CMvMPFE方法计算滚动轴承实验故障数据的熵值，并采用支持向量机（SVM）进行故障诊断分析。通过与MPFE、复合多尺度排列模糊熵（CMPFE）和多元多尺度排列模糊熵（MvMPFE）方法进行比较，计算结果表明，CMvMPFE方法能更全面、准确地提取滚动轴承故障特征，且具有良好的鲁棒性。
