Zhao Huimin, Yao Rui, Xu Ling, Yuan Yu, Li Guangyu, Deng Wu
Software Institute, Dalian Jiaotong University, Dalian 116028, China.
Chuzhou Technical Supervision and Testing Center, Chuzhou 239000, China.
Entropy (Basel). 2018 Sep 7;20(9):682. doi: 10.3390/e20090682.
A damage degree identification method based on high-order difference mathematical morphology gradient spectrum entropy (HMGSEDI) is proposed in this paper to solve the problem that fault signal of rolling bearings are weak and difficult to be quantitatively measured. In the HMGSEDI method, on the basis of mathematical morphology gradient spectrum and spectrum entropy, the changing scale influence of structure elements to damage degree identification is thoroughly analyzed to determine its optimal scale range. The high-order difference mathematical morphology gradient spectrum entropy is then defined in order to quantitatively describe the fault damage degree of bearing. The discrimination concept of fault damage degree is defined to quantitatively describe the difference between the high-order differential mathematical entropy and the general mathematical morphology entropy in order to propose a fault damage degree identification method. The vibration signal of motors under no-load and load states are used to testify the effectiveness of the proposed HMGSEDI method. The experiment shows that high-order differential mathematical morphology entropy can more effectively identify the fault damage degree of bearings and the identification accuracy of fault damage degree can be greatly improved. Therefore, the HMGSEDI method is an effective quantitative fault damage degree identification method, and provides a new way to identify fault damage degree and fault prediction of rotating machinery.
针对滚动轴承故障信号微弱且难以定量测量的问题，本文提出了一种基于高阶差分数学形态学梯度谱熵（HMGSEDI）的损伤程度识别方法。在HMGSEDI方法中，基于数学形态学梯度谱和谱熵，深入分析了结构元素变化尺度对损伤程度识别的影响，以确定其最佳尺度范围。然后定义高阶差分数学形态学梯度谱熵，以定量描述轴承的故障损伤程度。定义故障损伤程度的判别概念，以定量描述高阶微分数学熵与一般数学形态学熵之间的差异，从而提出一种故障损伤程度识别方法。利用电机在空载和负载状态下的振动信号验证了所提HMGSEDI方法的有效性。实验表明，高阶微分数学形态学熵能够更有效地识别轴承的故障损伤程度，大大提高了故障损伤程度的识别精度。因此，HMGSEDI方法是一种有效的定量故障损伤程度识别方法，为旋转机械故障损伤程度识别及故障预测提供了一种新途径。
