Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (4): 213-215.DOI: 10.3778/j.issn.1002-8331.2010.04.067

• 工程与应用 • Previous Articles     Next Articles

Application of wavelet neural network on rolling element bearings fault diagnosis

PENG Tao,MA Qian   

  1. Xiangtan University,Xiangtan,Hunan 411105,China
  • Received:2009-01-15 Revised:2009-03-23 Online:2010-02-01 Published:2010-02-01
  • Contact: PENG Tao

小波神经网络在滚动轴承故障诊断中的应用

彭 滔,马 茜   

  1. 湘潭大学,湖南 湘潭 411105
  • 通讯作者: 彭 滔

Abstract: Structure model and algorithms of Wavelet Neural Network(WNN) are designed combining the advantages of both wavelet transform and Artificial Neural Network(ANN).Compared with the slow convergence rate of conventional BP neural network,the algorithms using variable learning rate and momentum factor can be evidently improved.Wavelet neural network is applied to typical faults diagnosis of rolling element bearings.Experimental data obtained from 7216 tapered bearings experiment table is used to train the network,using vibration signal as its input.The simulation results indicate that the WNN can accurately classify faults.The WNN obviously surpasses the conventional BP neural network,and it efficiently accomplishes the fault diagnosis of rolling element bearings.

Key words: wavelet analysis, neural network, fault diagnosis

摘要: 结合小波变换和神经网络的优势给出小波神经网络的结构模型,研究了小波神经网络的学习算法;针对传统算法收敛速度慢等问题,从学习率和引入动量项两个方面对算法进行改进。应用小波网络对滚动轴承的典型故障进行实例诊断。以7216圆锥轴承在实验台上所测取的数据进行网络训练。用振动信号为网络输入向量,给出训练结果。仿真实例表明,采用小波神经网络能够很好地对故障进行分类,其收敛速度明显要快于相同条件BP神经网络,有效地实现了滚动轴承的故障诊断。

关键词: 小波分析, 神经网络, 故障诊断

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