基于排序模式相异性分析的轴承健康监测  

Bearing Health Monitoring Based on Ordinal Pattern Dissimilarity Analysis

作　　者：江国乾[1] 谢平[1] 王霄[1] 何群[1] 李继猛[1] 

机构地区：[1]燕山大学电气工程学院,秦皇岛066004

出　　处：《中国机械工程》2017年第6期714-720,727,共8页China Mechanical Engineering

基　　金：河北省高等学校科学技术研究重点项目(ZD20131080);国家自然科学基金资助项目(51505415);河北省自然科学基金资助项目(F2016203421);中国博士后科学基金资助项目(2015M571279)

摘　　要：排序模式分析方法通过相空间重构将一维振动时间序列映射到排序模式概率分布,来揭示序列内部结构的复杂性变化,为微弱信号特征提取提供了一种新视角。将排序模式分析和信息散度相结合,提出一种排序信息散度指标,用于对设备不同运行状态下的振动信号在高维相空间中排序模式概率分布的差异性进行量化分析,并用于轴承内圈不同损伤程度评估和轴承全寿命退化趋势分析。结果表明,与传统的时域统计指标及小波熵、近似熵、排序熵等非线性复杂度指标相比较,所提出的排序信息散度指标具有较好的故障程度量化分析性能,对轴承早期故障退化更加敏感,且稳定性好、计算效率高,利于工程实现。Ordinal pattern analysis might map one-dimensional vibration time series into probability distribution of ordinal patterns in the high-dimensional phase space,and then reveal the internal tiny variations in ordinal pattern structures.It provided a new research view for the weak vibration signal feature extraction.A new monitoring indicator named ordinal information divergence was proposed herein based on ordinal pattern analysis and information divergence,to quantitatively describe the ordinal pattern distribution difference of vibration signals in the high-dimensional phase space between the current status and the reference health status.Two experiments,including the damage degree assessment of bearing inner race and the run-to-failure bearing degradation trend analysis,were used to validate the effectiveness of the proposed new indicator.The comparative studies were performed with the traditional statistics and several existing nonlinear indicators including wavelet entropy,approximate entropy and permutation entropy.Experimental results demonstrate that the proposed new indicator presents better quantitative ability for different damage degrees and is more sensitive to the incipient fault and more stable and efficient in computation,thus easy to implement in engineering applications.

关 键 词：排序模式分析 信息散度 状态监测 滚动轴承 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]