基于BP神经网络的截齿磨损程度在线监测

中国机械工程

基于BP神经网络的截齿磨损程度在线监测

张强1,2,3;刘志恒1,4;王海舰1;黄传辉4;阮越宣1

1.辽宁工程技术大学机械工程学院，阜新，123000
2.大连理工大学工业装备结构分析国家重点实验室，大连，116023
3.四川理工学院材料腐蚀与防护四川省重点实验室，自贡，643000
4.徐州工程学院机电工程学院，徐州，221111

出版日期:2017-05-10 发布日期:2017-05-04
基金资助:
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)

On-line Monitoring of Pick's Wear Degrees Based on BP Neural Network

ZHANG Qiang1,2,3;LIU Zhiheng1,4;WANG Haijian1;HUANG Chuanhui4;Nguyen Viet Tuyen1

1.College of Mechanical Engineering, Liaoning Technical University,Fuxin, Liaoning,123000
2.State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, Liaoning,116023
3.Material Corrosion and Protection Key Laboratory of Sichuan Province,Sichuan University of Science & Engineering, Zigong,Sichuan,643000
4.College of Mechanical and Electrical Engineering,Xuzhou Instituteof Engineering, Xuzhou, Jiangsu,221111

Online:2017-05-10 Published:2017-05-04

摘要/Abstract

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。

关键词: 截齿, 磨损程度, 三向振动, 在线监测

Abstract: In order to realize the real-time monitoring degrees of pick wear in cutting processes, based on BP neural network a method was proposed for detection of multi-feature signal fusion. By extracting different wear degrees pick signals,such as three direction vibration signals, infrared temperature signals and current signals during the cutting processes, A database of multi-feature signal samples of different pick wear degree was established, the BP neural network was learned and trained by using the multi characteristic pick wear degress signal samples, and the pick wear a recognition model for pick wear degress was established to achieve online monitoring and recognition.The results show that: Based on the BP neural network, the network discrimination results of monitoring system for the degree of pick wear is consistent with the actual wear degree category of the test samples, the establishment of BP neural network system may accurately monitor and identify the type of wear degrees.

Key words: pick;wear degree, three direction vibration, online monitoring

中图分类号:

TP277

张强, 刘志恒, 王海舰, 黄传辉, 阮越宣. 基于BP神经网络的截齿磨损程度在线监测[J]. 中国机械工程.

ZHANG Qiang, LIU Zhiheng, WANG Haijian, HUANG Chuanhui, Nguyen Viet Tuyen. On-line Monitoring of Pick's Wear Degrees Based on BP Neural Network[J]. China Mechanical Engineering.

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