基于BP神经网络的截齿磨损程度在线监测

发布时间：2018-02-26 05:33

本文关键词： 截齿磨损程度三向振动在线监测　出处：《中国机械工程》2017年09期 　论文类型：期刊论文

【摘要】：为实现截齿截割过程中磨损程度的实时精准在线监测,提出了一种基于BP神经网络的截齿磨损程度多特征信号融合的检测方法。通过提取截割过程中不同磨损程度截齿的三向振动信号、红外温度信号和电流信号,建立了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BP神经网络进行学习和训练,建立截齿磨损程度的识别模型,实现截齿磨损程度在线监测与精确识别。实验结果表明:基于BP神经网络的截齿磨损程度监测系统,网络判别结果和测试样本的实际磨损程度类别相符,该BP神经网络系统能够对截齿磨损程度类型进行准确的监测和识别。
[Abstract]:In order to realize the real-time and accurate on-line monitoring of the wear degree in the process of cutting teeth, In this paper, a method of detecting multi-feature signal fusion of tooth cutting wear degree based on BP neural network is proposed. By extracting three direction vibration signal, infrared temperature signal and current signal of different wear degree gear cut, the paper presents a method of detecting tooth cutting wear degree based on BP neural network. The multi-feature signal sample database with different wear degree is established. The BP neural network is studied and trained by the multi-feature signal sample, and the recognition model of tooth cutting wear degree is established. The experimental results show that the network discriminating results are consistent with the actual wear degree categories of the test samples, and the experimental results show that the monitoring system based on BP neural network is consistent with the actual wear degree of the test samples. The BP neural network system can accurately monitor and identify the type of gear cutting wear.
【作者单位】：辽宁工程技术大学机械工程学院;大连理工大学工业装备结构分析国家重点实验室;四川理工学院材料腐蚀与防护四川省重点实验室;徐州工程学院机电工程学院;
【基金】：国家自然科学基金资助项目(51504121) 高等学校博士学科点专项科研基金资助项目(20132121120011) 材料腐蚀与防护四川省重点实验室开放基金资助项目(2014CL18) 工业装备结构分析重点实验室开放基金资助项目(GZ1402) 机械传动国家重点实验室开放基金资助项目(SKLMT-KFKT-201515)
【分类号】：TD421;TP183

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