• CN:11-2187/TH
  • ISSN:0577-6686

›› 2007, Vol. 43 ›› Issue (10): 137-143.

• 论文 • 上一篇    下一篇

基于神经网络的球轴承剩余寿命预测

奚立峰;黄润青;李兴林;刘中鸿;李杰   

  1. 上海交通大学机械与动力工程学院;杭州轴承试验研究中心;普度大学工业工程学院;辛辛纳提大学工学院
  • 发布日期:2007-10-15

RESIDUAL LIFE PREDICTIONS FOR BALL BEARING BASED ON NEURAL NETWORKS

XI Lifeng;HUANG Runqing;LI Xinglin;LIU C Richard;LEE Jay   

  1. School of Mechanical Engineering, Shanghai Jiaotong University Hangzhou Bearing Test & Research Center School of Industrial Engineering, Purdue University School of Engineering, University of Cincinnati
  • Published:2007-10-15

摘要: 针对球轴承的剩余寿命预测问题,基于自组织映射(Self organizing map, SOM)和反向传播 (Back propagation, BP)两种神经网络,提出一套新的预测球轴承剩余寿命的方法体系。深入对比分析几种不同轴承衰退指标的优缺点,利用三套时间域衰退指标和三套频率域衰退指标,包括一套新设计的指标,训练自组织映射神经网络。将源自于SOM的最小量化误差(Minimum quantization error, MQE)作为新的衰退指标,建立一套轴承性能数据库。针对球轴承衰退期,训练一套BP神经网络,根据权值计算失效时间技术,成功开发一套剩余寿命预测模型。结果表明,该方案远优于业界常用的L10寿命估计。

关键词: 球轴承, 神经网络, 剩余寿命, 预测模型, 自组织映射

Abstract: A new scheme for prediction of ball bearing’s remaining useful life is dealt with based on self-organizing map and back propagation neural networks. One of the key issues in bearing life prediction is to set up an appropriate degradation indicator from its incipient defect stage to final failure. Different from degradation features ever used, it uses the minimum quantization error (MQE) indicator deriving from SOM, which is trained by six vibrations features including a new designed degradation index for performance degradation assessment. Then using this indicator, back propagation neural networks focusing on the degradation periods are trained. Based on weight application to failure times (WAFT) technology, a remaining useful life prediction model of ball bearing is developed successfully. The validation results show that the proposed methods are greatly superior to the currently used L10 bearing life prediction.

Key words: Ball bearing, Neural network, Prediction model, Residual life, Self-organizing map

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