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浙江大学学报(工学版)
机械工程     
液压挖掘机作业循环阶段的智能识别
冯培恩, 彭贝, 高宇, 邱清盈
浙江大学 机械设计研究所,浙江 杭州 310027
Intelligent identification for working cycle stages of hydraulic excavator
FENG Pei en, PENG Bei, GAO Yu, QIU Qing ying
Institute of Mechanical Design, Zhejiang University, Hangzhou 310027, China
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摘要:
为了实现对液压挖掘机作业循环各阶段的自动识别,提出以主泵压力为识别对象的智能识别方法.以各阶段开始或结束时的一段波形作为分段标志,对作业循环进行分段.采用有向无环图支持向量机(DAGSVM)多分类方法,识别各分段标志,根据各类样本之间的可分度,优化DAGSVM结构,同时设定距离阈值,保证被识别出的波形为分段标志.引入智能校验系统,对由操作手误动作等引起的识别错误进行校正,使识别准确率由65%提高至95%.最后分析了分段标志宽度对识别准确率的影响.实际测试表明,该方法识别准确率高,实时性好,能够有效识别作业循环各阶段.
Abstract:
An intelligent identification method using the pump pressure as the identifying object was proposed in order to automatically identifythe each stage of a working cycle of hydraulic excavator. The segmentation of a working cycle was achieved by choosing the pressure curves at the beginning or the end of each stage as the segmentation marks which were recognized by the directed acyclic graph support vector machine (DAGSVM). The structure of DAGSVM was optimized according to the divisibility between the samples of each class. A distance threshold was set to ensure that the identified curves were the segmentation marks. An intelligent verifying system was introduced to correct the identification errors caused by incorrect operation so that the recognition accuracy rose from 65% to 95%. The relationship between the width of segmentation marks and the recognition accuracy was also investigated. Experimental results show that the proposed method can effectively identify the working cyclestages, with high recognition accuracy and good real time performance.
出版日期: 2016-02-01
:  TU 621  
基金资助:

国家“十一五”科技支撑计划资助项目(2013BAF07B04 03).

通讯作者: 高宇,男,实验师. ORCID: 0000 0003 0467 2333.     E-mail: zdgaoyu@zju.edu.cn
作者简介: 冯培恩(1943—),男,博导,主要从事为工程设计学、广义优化设计、CAD技术、工程机械机器人化技术等研究.ORCID: 0000 0002 9819 2674. E-mail: fpe@zju.edu.cn
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引用本文:

冯培恩, 彭贝, 高宇, 邱清盈. 液压挖掘机作业循环阶段的智能识别[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2016.02.003.

FENG Pei en, PENG Bei, GAO Yu, QIU Qing ying. Intelligent identification for working cycle stages of hydraulic excavator. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2016.02.003.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2016.02.003        http://www.zjujournals.com/eng/CN/Y2016/V50/I2/209

[1] 杨世平,余浩,刘金刚,等.液压挖掘机动力系统功率匹配及其节能控制[J].机械工程学报,2014,50(05): 152-160.
YANG Shi ping, Yu Hao, LIU Jin gang, et al. Research on power matching and energy saving control of power system in hydraulic excavator[J]. Journal of Mechanical Engineering, 2014, 50(05): 152-160.
[2] 雷延强,刘强.挖掘机液压系统节能控制的分析研究[J].机床与液压,2009, 37(8): 7172, 1-11.
LEI Yan qiang, LIU Qiang. Research on the energy saving of control system of hydraulic excavator [J]. Machine Tool & Hydraulics, 2009, 37(8): 7172, 1-11.
[3] 高峰,高宇,冯培恩.挖掘机载荷自适应节能控制策略[J].同济大学学报:自然科学版,2001, 29(9): 1036-1040.
GAO Feng, GAO Yu, FENG Pei en. Method of load matching control of hydraulic excavator’s energy saving[J]. Journal of Tongji University: Natural Science, 2001,29(9): 1036-1040.
[4] 柳齐.基于动作序列识别的挖掘机智能节能方法研究[D].厦门:华侨大学, 2014.
LIU Qi. Intelligent energy saving method research of excavator based on identifying the sequence of actions[D]. Xiamen: Huaqiao University, 2014.
[5] TANAKA M, DOISHITA K. Method and system for supporting fuel cost saving operation of construction machine: JP, 082506[P]. 20081009.
[6] 刘志东,李莺莺,杨清淞,等.挖掘机液压系统载荷数据测试方法研究[J].工程机械,2013, 44(3): 18-25.
LIU Zhi dong, LI Ying ying, YANG Qing song, et al. Research on test methods of payload data of the excavator’s hydraulic system[J]. Construction Machinery and Equipment, 2013, 44(3): 18-25.
[7] WUTKE J, DORSETT W A, BARR M N, et al. Energy management system for machinery performing a predictable work cycle: US, 0083089[P]. 20140327.
[8] YANG J, QUAN L, YANG Y. Excavator energy saving efficiency based on diesel engine cylinder deactivation technology[J]. Chinese Journal of Mechanical Engineering, 2012, 25(5): 897-904.
[9] 郝鹏,何清华,张新海,等.挖掘机负载和工况识别技术研究[J].液压气动与密封,2008(5): 8-13.
HAO Peng, HE Qing hua, ZHANG Xin hai, et al. Study on load and operating mode identification of excavator[J]. Hydraulics Pneumatics & Seals, 2008(5): 8-13.
[10] TIMUSK M A, LIPSETT M G, MCBAIN J, et al. Automated operating mode classification for online monitoring systems[J]. Journal of Vibration and Acoustics, 2009(8): 131-140.
[11] MINTAH B, PRICE R J, KING K D, et al. Adaptive work cycle control system: US, 8024095[P]. 20110920.
[12] 于洪光.液压挖掘机典型负载工况研究[D].杭州: 浙江工业大学, 2012.
YU Hong guang. Research on typical load condition of hydraulic excavator[D]. Hangzhou: Zhejiang University of Technology, 2012.
[13] 高峰.液压挖掘机节能控制技术的研究[D].杭州:浙江大学, 2001.
GAO Feng. Research on energy saving control of hydraulic excavator[D]. Hangzhou: Zhejiang university,2001.
[14] LI H, ZHOU P, ZHANG Z. An investigation into machine pattern recognition based on time frequency image feature extraction using a support vector machine[J]. Proceedings of The Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 2010, 224: 981-994.
[15] 任峰,赵志敏,张林,等.加权均值滤波在机场跑道路面裂纹图像检测中的应用[J].理化检验、物理分册,2012(07): 428-431.
REN Feng, ZHAO Zhi min, ZHANG Lin, et al. Application of weigthed mean filter in image analysis of pavement cracks on airport field runway[J]. Physical Testing and Chemical Analysis Part A: Physical Testing, 2012(07): 428-431.
[16] 朱波,刘飞,李顺江.基于优化有向无环图支持向量机的多变量过程均值异常识别[J].计算机集成制造系统, 2013(03): 559-568.
ZHU bo, LIU Fei, LI Shun jiang. Mean abnormality identification in multivariate process based on optimized directed acyclic graph support vector machine[J]. Computer Integrated
Manufacturing Systems, 2013, 19(03): 559-568.
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