中国机械工程 ›› 2014, Vol. 25 ›› Issue (5): 602-607.

• 机械基础工程 • 上一篇    下一篇

基于VPANNs的掘进机回转台可靠性分析

赵丽娟1;刘旭南1;孙强2   

  1. 1.辽宁工程技术大学,阜新,123000
    2.兖矿集团有限公司机电设备制造厂,邹城,273500
  • 出版日期:2014-03-10 发布日期:2014-03-21
  • 基金资助:
    “十一五”国家科技支撑计划项目(2007BAF12B00);辽宁省教育厅高等学校创新团队资质项目(2007T070)

Research on Road Header Gyration Platform's Reliability Based on VPANNs

Zhao Lijuan1;Liu Xunan1;Sun Qiang2   

  1. 1.Liaoning Technical University,Fuxin,Liaoning,123000
    2.Electromechanical Equipment Manufacturing Plant of Yankuang Group,Zoucheng,Shandong,273500
  • Online:2014-03-10 Published:2014-03-21
  • Supported by:
    The National Key Technology R&D Program(No. 2007BAF12B00)

摘要:

以某型掘进机可靠性研究项目为依托,基于破岩机理,利用MATLAB编制了掘进机在不同横摆速度、截割位置角度下截割不同坚固性系数岩石的程序并生成载荷文本,在动力学仿真软件ADAMS中建立了掘进机截岩的虚拟样机模型,并对不同工况载荷下的模型进行了动态仿真。采用模糊方法拟合了掘进机回转台的可靠度隶属函数,以各工况参数及相应的可靠度隶属函数值作为训练样本,提出了一种将人工神经网络与虚拟样机技术相结合分析零件可靠性的新方法。结合神经网络的推理可以预测掘进机在新工况下工作时回转台的可靠度,预测均方误差达到了6.92×10-6,对实际生产具有一定的指导意义。

关键词: 掘进机, 虚拟样机技术, 回转台, 人工神经网络, 可靠性

Abstract:

Taking the project of a type of road header's reliability research as support, based on rock cutting mechanism, a program of road header cutting different consistent coefficient rocks at different horizontal swing velocities with different cutting position angles were generated and the load files were produced by MATLAB. A virtual prototype model of road header cutting rocks was built and dynamics simulations under different working load conditions were done in dynamics simulation software ADAMS. Using fuzzy method, the reliability degree membership function of road header's gyration platform was fitted. Taking different working conditions and relative reliability degree membership function as training samples, a new method combined ANNs and virtual prototype technology was proposed to analyse the reliability. Refer to the reason of ANNs, gyration platform's reliability degree can be forecasted when the road header is working in new conditions, the prediction mean squared errors reach 6.92×10-6, this method also has instructive significance to practical productions.

Key words: road header, virtual prototype(VP) technology, gyration platform, artificial neural network(ANN), reliability

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