ISSN   1004-0595

CN  62-1224/O4

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用人工神经网络预测摩擦学系统磨损趋势

Forecasting of the Wear Trend of Tribosystems Using Artificial Neural Networks

  • 摘要: 人工神经网络具有高度的并行分布式、联想记忆、自组织及自学习能力和极强的非线性映射能力,在许多领域显示了广阔的应用前景.但是,将神经网络用于摩擦学行为预测的研究报道却还鲜见.在对基于神经网络的单变量时间序列预测方法与过程进行分析之后,提出了摩擦学系统磨损趋势神经网络预测模型.采用定量铁谱参数中的总磨损Q作为预测磨损趋势的特征参数,讨论了磨损趋势神经网络预测的单步预测法和多步预测法,并用其对CD40齿轮泵的磨损趋势进行了预测,预测值与实测值吻合较好

     

    Abstract: For the parallel distributed processing,associative memory,self organization,self learning and strong mapping abilities,artificial neural networks have shown broad application prospects in many fields. The method and procedure of single variable time series forecasting based on neural networks are described.The forecasting model for the wear trend of tribosystems is proposed,in which the quantitative ferrographic parameter Q is used as the characteristic parameter for forecasting wear trend.The single step and multi step forecasting method for the wear trend forecasting based on the neural network are discussed.The model is applied to the wear trend of the gear pump.The results are very satisfactory.

     

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