Xu Zhuofei, Shi Yuxia, Zhao Qinghai, Li Wei, Liu Kai
Faculty of Printing Packaging Engineering and Digital Media Technology, Xi'an University of Technology, Xi'an 710048, China.
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China.
Entropy (Basel). 2019 Mar 2;21(3):238. doi: 10.3390/e21030238.
Self-adaptive methods are recognized as important tools in signal process and analysis. A signal can be decomposed into a serious of new components with these mentioned methods, thus the amount of information is also increased. In order to use these components effectively, a feature set is used to describe them. With the development of pattern recognition, the analysis of self-adaptive components is becoming more intelligent and depend on feature sets. Thus, a new feature is proposed to express the signal based on the hidden property between extreme values. In this investigation, the components are first simplified through a symbolization method. The entropy analysis is incorporated into the establishment of the characteristics to describe those self-adaptive decomposition components according to the relationship between extreme values. Subsequently, Extreme Interval Entropy is proposed and used to realize the pattern recognition, with two typical self-adaptive methods, based on both Empirical Mode Decomposition (EMD) and Empirical Wavelet Transform (EWT). Later, extreme interval entropy is applied in two fault diagnosis experiments. One experiment is the fault diagnosis for rolling bearings with both different faults and damage degrees, the other experiment is about rolling bearing in a printing press. The effectiveness of the proposed method is evaluated in both experiments with K-means cluster. The accuracy rate of the fault diagnosis in rolling bearing is in the range of 75% through 100% using EMD, 95% through 100% using EWT. In the printing press experiment, the proposed method can reach 100% using EWT to distinguish the normal bearing (but cannot distinguish normal samples at different speeds), with fault bearing in 4 r/s and in 8 r/s. The fault samples are identified only according to a single proposed feature with EMD and EWT. Therefore, the extreme interval entropy is proved to be a reliable and effective tool for fault diagnosis and other similar applications.
自适应方法被认为是信号处理与分析中的重要工具。利用这些方法可将信号分解为一系列新的分量，从而增加信息量。为有效利用这些分量，需用一个特征集来描述它们。随着模式识别的发展，对自适应分量的分析变得更加智能且依赖于特征集。因此，基于极值间的隐藏特性提出了一种新的特征来表示信号。在本研究中，首先通过一种符号化方法简化分量。根据极值间的关系，将熵分析纳入特征的建立中，以描述那些自适应分解分量。随后，提出了极值区间熵并将其用于基于经验模态分解（EMD）和经验小波变换（EWT）这两种典型自适应方法的模式识别。之后，将极值区间熵应用于两个故障诊断实验。一个实验是对具有不同故障和损伤程度的滚动轴承进行故障诊断，另一个实验是关于印刷机中的滚动轴承。在这两个实验中均使用K均值聚类评估所提方法的有效性。使用EMD时，滚动轴承故障诊断的准确率在75%至100%之间；使用EWT时，准确率在95%至100%之间。在印刷机实验中，使用EWT区分正常轴承（但无法区分不同转速下的正常样本）以及4转/秒和8转/秒的故障轴承时，所提方法可达100%。仅根据EMD和EWT所提的单个特征就能识别故障样本。因此，极值区间熵被证明是故障诊断及其他类似应用的可靠且有效工具。
