• 论文
主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
基于PNN神经网络的掘进机截齿磨损程度识别研究
  • Title

    Research on identification of pick wear degree of roadheader based on PNN neural network

  • 作者

    张强王禹王琛淇

  • Author

    ZHANG Qiang,WANG Yu,WANG Chenqi

  • 单位

    辽宁工程技术大学机械工程学院

  • Organization
    Liaoning Technical University,Fuxin ,China
  • 摘要

    针对掘进机截齿磨损在线识别问题,提出一种基于PNN神经网络的截齿磨损程度多特征信号识别方法,提取不同磨损程度的截齿在截割过程中的振动和声发射特征信号,分别分析振动加速度、声发射信号峰值以及2种特征信号频域图的均方根这4个特征参数,获取振动信号、声发射信号与不同磨损程度截齿的变化规律。建立5种不同磨损程度截齿的多特征信号样本数据库,采用多特征信号样本对PNN神经网络进行学习和训练,建立截齿磨损程度的识别模型,实现截齿磨损程度的精确识别。结果表明:基于PNN神经网络的截齿预测磨损状态识别模型识别精度较高,识别准确率和预测准确率约为93.3%和95%,与BP神经网络方法相比分别提高了3.3%和15%。因此该神经网络具有良好的可靠性与精确性。

  • Abstract
    In view of the problem of on-line identification of cutting wear of roadheader, a multi-feature signals recognition method based on PNN neural network is proposed.The vibration and acoustic emission characteristic signals of the picks with different pick wear degrees during the cutting process are extracted and analyzed separately.The four characteristic parameters of the vibration acceleration, the peak value of acoustic emission signal and the root mean square of the frequency domain diagram of two characteristic signals were used to obtain the variation of vibration signal, acoustic emission signal and pick of different wear degrees.A Multi-feature signal sample database for five different pick wear degree were established.The multi-feature signal samples were used to study and train the PNN neural network, the recognition model of pick wear degree was established to realize the accurate identification of pick wear degree.The results show that the recognition accuracy of model based on PNN neural network has higher recognition accuracy, and the accuracy of recognition and prediction are about 93.3% and 95%, which is 3.3% and 15% higher than that of BP neural network.The results provide an important technical means for identifying the wear degree of picks precisely and improving the work efficiency.
  • 关键词

    掘进机截齿磨损振动信号声发射信号PNN神经网络

  • KeyWords

    roadheader; pick wear; vibration signal; acoustic emission signal;PNN neural network

  • 基金项目(Foundation)
    国家自然科学基金资助项目(51504121);国家自然科学基金资助项目(51774161)
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主办单位:煤炭科学研究总院有限公司 中国煤炭学会学术期刊工作委员会

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