• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2015, Vol. 51 ›› Issue (9): 97-103.doi: 10.3901/JME.2015.09.097

• 机械动力学 • 上一篇    下一篇

用灰自助泊松方法预测滚动轴承振动性能可靠性的变异过程

夏新涛, 孟艳艳, 邱明   

  1. 河南科技大学机电工程学院
  • 出版日期:2015-05-05 发布日期:2015-05-05
  • 基金资助:
    国家自然科学基金(51475144, 51075123)和河南省高校科技创新团队支持计划(13IRTSTHN025)资助项目

Forecasting for Variation Process of Reliability of Rolling Bearing Vibration Performance Using Grey Bootstrap Poisson Method

XIA Xintao, MENG Yanyan, QIU Ming   

  1. School of Mechatronical Engineering, Henan University of Science and Technology
  • Online:2015-05-05 Published:2015-05-05

摘要: 将灰自助原理融入泊松过程,提出灰自助泊松方法,以预测滚动轴承振动性能可靠性的变异过程。凭借时间序列的计数过程,在短时间区间内获取轴承振动表现出的变异强度的极少量原始信息;经过对变异强度原始信息的自助再抽样,模拟出变异强度的大量生成信息;用灰预测模型处理生成信息,获取变异强度估计值;用泊松过程表征可靠性函数,实时预测轴承振动性能可靠性的变异过程。轴承振动时间序列可靠性的试验研究表明,性能可靠性变异状态可以被真实描述,预测值与检验值具有很好的一致性。

关键词: 变异过程, 滚动轴承, 灰自助泊松方法, 可靠性, 时间序列, 振动

Abstract: Fusing the grey bootstrap principle into Poisson process, the grey bootstrap Poisson method is proposed to forecast the variation process of reliability of the rolling bearing vibration performance. A small number of raw variation-intensity information presented by bearing vibration is extracted with the help of the counting process of time series in short time interval, a large number of generated variation-intensity information is simulated by means of bootstrap resampling from raw variation-intensity information, the estimated value of variation intensity is obtained by using the grey prediction model to process generated variation-intensity information, and the variation process of reliability of the bearing vibration performance is forecasted in time via the reliability function expressed as Poisson process. Experimental investigation on reliability of bearing vibration as a time series shows that variable states of performance reliability can be described truly and predicted values are in very good accordance with test values.

Key words: grey bootstrap Poisson method, reliability, rolling bearing, time series, variation process, vibration

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