北京航空航天大学学报 ›› 2009, Vol. 35 ›› Issue (10): 1161-1165.

• 论文 •    下一篇

基于小波包和BP网络的滚动轴承状态寿命模型

苗学问, 洪 杰, 马艳红   

  1. 北京航空航天大学 能源与动力工程学院, 北京 100191
  • 收稿日期:2008-09-27 出版日期:2009-10-31 发布日期:2010-09-16
  • 作者简介:苗学问(1977-),男,河南周口人,博士生,xwmiao77@sohu.com.
  • 基金资助:

    航空科学基金资助项目(2007ZB51021)

Grade-life model based on wavelet package and BP network for rolling bearing

Miao Xuewen, Hong Jie, Ma Yanhong   

  1. School of Jet Propulsion, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2008-09-27 Online:2009-10-31 Published:2010-09-16

摘要: 使用状态寿命(即状态良好、初始损伤、故障发展和即将失效4个寿命阶段)描述滚动轴承的使用寿命,并建立了滚动轴承状态寿命的评估模型.状态寿命评估模型建模的关键是振动信号的特征提取和状态的识别算法.针对滚动轴承振动的特点,根据小波包变换能将信号按任意时频分辨率分解到不同频段的特性,提取小波包重构信号的频带能量构造特征矢量,利用推广性能良好的贝叶斯正则化BP网络作为状态寿命评估的算法建立特征向量与状态寿命之间的映射,采用滚动轴承全寿命试验数据作为学习样本,训练和确定评估模型.试验验证了模型的有效性和可信性.

Abstract: Grade-life was used to describe rolling bearing-s service life, which means the entire service life is divided into four stages: good bearing condition, initial defect condition, damaged bearing condition and failure coming condition, and an assessment model was presented for bearing-s Grade-life. Signal feature extraction and pattern recognition algorithm are keys to construct the model. Vibration signals of the rolling bearing were analyzed, and the wavelet packet analysis theory was adopted to extract the Grade-life characteristics. Through signal decomposition and single reconfiguration with wavelet package to extract feature in every frequency bands effectively, and energy of various frequency bands acting as the life feature vector was input into the improved BP neural network to realize the mapping between the Grade-life vector and the Grade-life of rolling bearing, and the model in establishing the identification model by using bearing test stand run-to-failure data as learning samples was employed. The validity and creditability of model has been demonstrated by bearing test stand dates.

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