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特征融合与灰色回归的滚动轴承性能退化评估
Feature Fusion and Grey Regression for Performance Degradation Assessment of Rolling Bearings

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杨创艳 1,2   马军 1,2 *   王晓东 1,2   罗亭 1,2   李卓睿 1,2  
文摘 针对传统退化指标无法准确反映滚动轴承全寿命周期内退化状态的问题,提出一种特征融合与灰色回归的滚动轴承性能退化评估方法.该方法提取滚动轴承振动信号的高维退化特征,构建基于单调性、相关性和鲁棒性的综合评价准则,选择有效退化特征并构建敏感指标集;提出核独立成分分析(Kernel Independent Component Analysis,KICA)和马氏距离(Mahalanobis Distance,MD)相结合的方法,计算敏感退化指标KICAMD;融合灰色回归模型和3δ原则,判定敏感退化指标KICAMD是否存在虚假波动并修复,获得轴承健康指标(Health Index,HI);最后,基于HI时间序列的转折突变点,自适应确定初始故障时间和定量评估轴承退化状态.两组滚动轴承全寿命周期振动实验数据及对比分析表明,所提方法构建的性能退化指标能有效表征轴承全生命周期的运行状态.
其他语种文摘 Aiming at the problem that the traditional degradation indicator cannot accurately reflect the degradation state of the rolling bearing in the whole life cycle, a method of performance degradation evaluation based on feature fusion and grey regression is proposed. The high-dimensional degradation features of vibration signal are extracted, and the comprehensive evaluation criteria based on monotonicity, correlation and robustness is constructed. A method combining kernel independent component analysis(KICA) and mahalanobis distance(MD) is proposed to calculate the sensitive degradation indicator KICAMD. Then, a novel based-the gray regression model and 3δ principle method is introduced to determine in advance whether the sensitivity degradation indicator KICAMD is false fluctuation ahead of time repair, and then the bearing degradation health indicator HI is obtained. Based on the abrupt transition point of HI time series, the start failure time is determined adaptively and the rolling bearing degradation state is quantitatively evaluated. The experiment and comparative analysis of two groups of rolling bearing life cycle vibration show that the constructed performance degradation index can effectively characterize the running state of the rolling bearing.
来源 电子学报 ,2022,50(1):106-115 【核心库】
DOI 10.12263/DZXB.20200826
关键词 滚动轴承 ; 特征融合 ; 灰色回归模型 ; 初始故障时间 ; 退化状态定量评估
地址

1. 昆明理工大学信息工程与自动化学院, 云南, 昆明, 650500  

2. 昆明理工大学, 云南省人工智能重点实验室, 云南, 昆明, 650500

语种 中文
文献类型 研究性论文
ISSN 0372-2112
学科 电子技术、通信技术
基金 国家自然科学基金 ;  云南省科技计划项目
文献收藏号 CSCD:7169563

参考文献 共 31 共2页

1.  郑近德. 基于改进经验小波变换的时频分析方法及其在滚动轴承故障诊断中的应用. 电子学报,2018,46(2):358-364 CSCD被引 14    
2.  潘海洋. 基于CELCD和MFVPMCD的智能故障诊断方法研究. 电子学报,2017,45(3):546-551 CSCD被引 8    
3.  苏维均. 基于局部频谱的滚动轴承故障特征提取方法. 电子学报,2018,46(1):160-166 CSCD被引 5    
4.  陈祥龙. 基于平方包络谱相关峭度的最优共振解调诊断滚动轴承故障. 机械工程学报,2018,54(21):90-100 CSCD被引 13    
5.  Zeng Z G. A hierarchical decision-making framework for the assessment of the prediction capability of prognostic methods. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability,2017,231(1):36-52 CSCD被引 1    
6.  郑小霞. 基于小波包和并行隐马尔科夫的风力机易损部件健康状态评价. 太阳能学报,2019,40(2):370-379 CSCD被引 6    
7.  Xu L. A new method for the estimation of bearing health state and remaining useful life based on the moving average cross-correlation of power spectral density. Mechanical Systems and Signal Processing,2020:139 CSCD被引 1    
8.  Shao H D. Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing. Knowledge-Based Systems,2020:88 CSCD被引 1    
9.  王斐. 基于VMD和SVDD的滚动轴承早期微弱故障检测和性能退化评估研究. 振动与冲击,2019,38(22):224-230,256 CSCD被引 12    
10.  郑近德. 基于复合多尺度模糊熵的滚动轴承故障诊断方法. 振动与冲击,2016,35(8):116-123 CSCD被引 12    
11.  戴豪民. 基于WMRMR的滚动轴承混合域特征选择方法. 振动与冲击,2015,34(19):57-61 CSCD被引 5    
12.  Zhu K H. Rolling element bearing fault diagnosis based on multi-scale global fuzzy entropy, multiple class feature selection and support vector machine. Transactions of the Institute of Measurement and Control,2019,41(14):4013-4022 CSCD被引 2    
13.  王奉涛. 流形模糊C均值方法及其在滚动轴承性能退化评估中的应用. 机械工程学报,2016,52(15):59-64 CSCD被引 12    
14.  Liu P. A novel non-uniform control vector parameterization approach with time grid refinement for flight level tracking optimal control problems. ISA Transactions,2018,73:66-78 CSCD被引 2    
15.  Tian J. Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE Transactions on Industrial Electronics,2016,63(3):1793-1803 CSCD被引 13    
16.  Wu J. Degradation data-driven time-tofailure prognostics approach for rolling element bearings in electrical machines. IEEE Transactions on Industrial Electronics,2019,66(1):529-539 CSCD被引 11    
17.  Sun H. A fault feature extraction method for single-channel signal of rotary machinery based on VMD and KICA. Journal of Vibroengineering,2019,21(2):370-383 CSCD被引 1    
18.  Wu J. Degradation data-drive time-to-failure prognostics approach for rolling element bearings in electrical machines. IEEE Transactions on Industrial Electronics,2018,66(1):529-539 CSCD被引 3    
19.  Ahmad W. A hybrid prognostics technique for rolling element bearings using adaptive predictive models. IEEE Transactions on Industrial Electronics,2017,65(2):1577-1584 CSCD被引 12    
20.  高彩霞. 线性回归与EEMD的滚动轴承剩余寿命预测. 机械科学与技术,2019,38(10):1589-1597 CSCD被引 5    
引证文献 2

1 张龙 采用显式动力学的轴承性能退化评估指标构建 西安交通大学学报,2022,56(8):11-21
CSCD被引 2

2 蔡曜 陀螺电机轴承小样本非等间隔的寿命预测研究 兵工学报,2024,45(7):2426-2441
CSCD被引 0 次

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