Gan Xiong, Lu Hong, Yang Guangyou
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.
Institute of Agricultural Machinery, Hubei University of Technology, Wuhan 430068, China.
Entropy (Basel). 2019 Mar 18;21(3):290. doi: 10.3390/e21030290.
This paper proposes a new method named composite multiscale fluctuation dispersion entropy (CMFDE), which measures the complexity of time series under different scale factors and synthesizes the information of multiple coarse-grained sequences. A simulation validates that CMFDE could improve the stability of entropy estimation. Meanwhile, a fault recognition method for rolling bearings based on CMFDE, the minimum redundancy maximum relevancy (mRMR) method, and the k nearest neighbor (kNN) classifier (CMFDE-mRMR-kNN) is developed. For the CMFDE-mRMR-kNN method, the CMFDE method is introduced to extract the fault characteristics of the rolling bearings. Then, the sensitive features are obtained by utilizing the mRMR method. Finally, the kNN classifier is used to recognize the different conditions of the rolling bearings. The effectiveness of the proposed CMFDE-mRMR-kNN method is verified by analyzing the standard experimental dataset. The experimental results show that the proposed fault diagnosis method can effectively classify the conditions of rolling bearings.
本文提出了一种名为复合多尺度波动散度熵（CMFDE）的新方法，该方法在不同尺度因子下测量时间序列的复杂性，并综合多个粗粒度序列的信息。仿真验证了CMFDE可以提高熵估计的稳定性。同时，开发了一种基于CMFDE、最小冗余最大相关度（mRMR）方法和k近邻（kNN）分类器的滚动轴承故障识别方法（CMFDE-mRMR-kNN）。对于CMFDE-mRMR-kNN方法，引入CMFDE方法提取滚动轴承的故障特征。然后，利用mRMR方法获得敏感特征。最后，使用kNN分类器识别滚动轴承的不同状态。通过分析标准实验数据集验证了所提出的CMFDE-mRMR-kNN方法的有效性。实验结果表明，所提出的故障诊断方法能够有效地对滚动轴承的状态进行分类。
