Yan Xiaoan, Xu Yadong, Jia Minping
School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, China.
School of Mechanical Engineering, Southeast University, Nanjing 211189, China.
Entropy (Basel). 2021 Aug 30;23(9):1128. doi: 10.3390/e23091128.
The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.
基于模糊熵的复杂度度量方法在轴承故障诊断中取得了丰硕成果。然而，传统的分层模糊熵（HFE）和多尺度模糊熵（MFE）仅在不同层次或尺度上挖掘轴承故障信息，而未同时考虑多层和多尺度上的轴承故障信息，从而容易导致故障信息提取不完整以及识别准确率较低。此外，大多数现有的基于熵的复杂度度量方法的关键参数是基于专家经验选择的，这表明它们缺乏自适应性。为了解决这些问题，本文提出了一种基于自适应分层多尺度模糊熵的新型智能轴承故障诊断方法。一方面，通过融合HFE和MFE的优点，提出了一种名为分层多尺度模糊熵（HMFE）的新型复杂度度量方法，以提取原始轴承振动信号的多维特征矩阵，其中HMFE的重要参数通过鸟群算法（BSA）自动确定。另一方面，引入了一种具有强鲁棒性的非线性特征矩阵分类器——支持矩阵机（SMM），用于直接从提取的多维特征矩阵中学习判别故障信息并自动识别不同的轴承健康状态。两项轴承故障诊断实验结果表明，所提方法的平均识别准确率分别可达99.92%和99.83%，高于本文报道的几种代表性熵的准确率。此外，在这两项实验中，所提方法识别准确率的标准差分别为0.1687和0.2705，也大于本文提及的对比方法的标准差。实验结果验证了所提方法的有效性和优越性。
