Application of HDLMD and JRD in Performance Evaluation of Rolling Bearing
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摘要: 针对微分局部均值分解(Differential local mean decomposition, DLMD)方法中微分次数计算缺乏理论指导以及传统性能退化指标无法准确表示滚动轴承在全寿命阶段上当前状态的问题, 提出了一种基于HDLMD(Hilbert-differential local mean decomposition, HDLMD)和JRD(Jensen-Renyi divergence)的滚动轴承性能评估方法。该方法首先对原始振动信号进行HDLMD分解, 提取乘积函数(Product function, PF)矩阵; 然后, 基于拉普拉斯分值(Laplacian score, LS)选择包含最多故障信息的PF分量; 再计算筛选之后的有效PF分量的概率分布, 得到有效PF分量的Renyi熵值; 最后, 计算正常信号与不同故障程度信号之间的JRD距离, 并判断滚动轴承的退化状态。通过凯西斯储大学(Case western reserve university, CWRU)滚动轴承实验数据和NASA(National aeronautics and space administration)全寿命周期数据实验表明, 本文所提方法可以准确、有效地评估轴承性能的退化状态。Abstract: Due to the lack of theoretical guidance in differential local mean decomposition (DLMD) method and the inability of traditional performance degradation index to accurately represent the current state of rolling bearing in the whole life stage, a new rolling bearing performance evaluation method based on Hilbert-differential local mean decomposition (HDLMD) and Jensen-Renyi divergence (JRD) was proposed. Firstly, the original vibration signal is decomposed by HDLMD and the product function matrix (PF) is extracted. Then, the PF component containing the most fault information is selected based on Laplacian score. Then calculate the probability distribution of the effective PF component after filtering, and obtain the Renyi entropy value of the effective PF component. Finally, the JRD distance between normal signal and signal of different fault degree is calculated, and the degenerate state of rolling bearing is judged. The rolling bearing experimental data from Case Western Reserve University and the life-cycle data from the National Aeronautics and Space Administration show that the proposed method can accurately and effectively evaluate the state of bearing performance degradation.
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表 1 各PF分量的拉普拉斯分值LS
PF1 PF2 PF3 PF4 PF5 PF6 PF7 0.767 9 0.816 4 0.643 6 0.977 7 1.988 3 4.829 5 6.575 9 表 2 HDLMD和LMD分解的JRD方差对比
方法 方差 HDLMD分解JRD 5.390 7×10-4 LMD分解JRD 9.355 1×10-4 -
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