基于多级数据融合技术的液压支架故障诊断技术
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摘要
为提高液压支架故障诊断技术水平,促进综采工作面智能化技术发展,在分析液压支架常见故障的基础上,将常见故障分为3个等级,设计了液压支架状态监测传感器布置方案,针对3个等级故障将传感器输出数据进行深度融合,即数据层融合、特征层融合和决策层融合,提出了一种基于多级数据融合技术的液压支架故障诊断方法。基于AMEsim软件平台建立了故障仿真模型,对该诊断方法进行了有效性验证。结果表明:该方法可实现液压支架故障的有效诊断,诊断结果可靠性高,实时性强,能够有效降低工作面设备故障的检修时间。
In order to improve the fault diagnosis technology of the hydraulic powered supports and to promote the intelligent technology development of the fully mechanized coal mining face,based on the analysis on the common failures of the hydraulic powered supports,the common failures were divided into three grades. A layout plan of the status monitoring and measuring sensors on the hydraulic powered supports were designed. According to the three grade failures,the output data of the sensors should have a deep fusion,including the date layer fusion,the feature layer fusion and the decision layer fusion and the fault diagnosis method of the hydraulic powered supports was provided based on the multilevel data fusion technology. And based on the AMESim software platform,the failure simulation model was established and the effective verification was conducted on the diagnosis method. The results showed that the diagnosis method could realize the effective diagnosis on the failure of the hydraulic powered supports,the diagnosis results could be high reliable,the real time would be high and the diagnosis results could effectively reduce the failure detection and repair time of the equipment applied in the coal mining face.
In order to improve the fault diagnosis technology of the hydraulic powered supports and to promote the intelligent technology development of the fully mechanized coal mining face,based on the analysis on the common failures of the hydraulic powered supports,the common failures were divided into three grades. A layout plan of the status monitoring and measuring sensors on the hydraulic powered supports were designed. According to the three grade failures,the output data of the sensors should have a deep fusion,including the date layer fusion,the feature layer fusion and the decision layer fusion and the fault diagnosis method of the hydraulic powered supports was provided based on the multilevel data fusion technology. And based on the AMESim software platform,the failure simulation model was established and the effective verification was conducted on the diagnosis method. The results showed that the diagnosis method could realize the effective diagnosis on the failure of the hydraulic powered supports,the diagnosis results could be high reliable,the real time would be high and the diagnosis results could effectively reduce the failure detection and repair time of the equipment applied in the coal mining face.
引文
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[19]韩寿松,刘相波,晁智强,等.基于数据融合技术的液压系统故障诊断[J].装甲兵工程学院学报,2005,19(1):57-61.Han Shousong,Liu Xiangbo,Chao Zhiqiang,et al.Fault diagnosis of hydraulic system based on data fusion technology[J].Journal of Academy of Aramorde Force Engineering,2005,19(1):57-61.
[20]张宪宇,陈小虎,何庆飞.基于AMESim的液压缸故障建模与仿真[J].液压气动与密封,2011,31(10):26-28.Zhang Xianyu,Chen Xiaohu,He Qingfei.Modeling and simulation for fault of hydraulic actuator based on AMESim[J].Hydraulics Pneumatics&Seals,2011,31(10):26-28.
[21]周小军,姜乐华,高经纬.基于AMESim的液压缸内泄漏仿真分析[J].机床与液压,2012,40(23):147-150.Zhou Xiaojun,Jiang Lehua,Gao Jingwei.Simulation analysis for inner leakage of hydraulic cylinder based on AMESim[J].Machine Tool&Hydraulics,2012,40(23):147-150.
[2]Zhao Xiuxu,Hu Zhemin,Shang Yanchen.Research on hydraulic cylinder fault diagnosis system based on ARM[J].Advanced Materials Research,2012,619:489-493.
[3]Cao Keqiang,Zhang Jianbang,Hu Liangmou.Fault diagnosis of electro-hydraulic position servo closed-loop system based on support vector regression[C]//IEEE International Conference on Automation and Logistics,Jinan,2007:3044-3049.
[4]章培军.基于小波分析液压支架的故障诊断研究[J].煤矿机械,2013,34(9):308-309.Zhang Peijun.Study on fault diagnosis of hydraulic support based on wavelet analysis[J].Coal Mine Machinery,2013,34(9):308-309.
[5]张东来,马鑫.基于驱动端电流的矿用液压电磁阀缓变失效预测方法[J].电子学报,2010,38(12):2805-2809.Zhang Donglai,Ma Xin.Prediction method for mining hydraulic electromagnetic valve degradation failure based on driving current[J].Acta Electronica Sinica,2010,38(12):2805-2809.
[6]Jegadeeshwaran R,Sugumaran V.Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines[J].Mechanical Systems&Signal Processing,2015,52(1):436-446.
[7]Sakthivel N R,Sugumaran V,Nair B B.Application of Support Vector Machine(SVM)and Proximal Support Vector Machine(PSVM)for fault classification of monoblock centrifugal pump[J].International Journal of Data Analysis Techniques&Strategies,2010,2(1):38-61.
[8]倪文婧.矿井提升机远程监控及故障诊断系统设计[J].煤炭科学技术,2013,41(2):81-83.Ni Wenjing.Design on remote monitoring and fault diagnosis system of mine hoist[J].Coal Science and Technology,2013,41(2):81-83.
[9]秦邦振,秦杰,李爱生,等.基于PAC的煤矿主扇风机远程综合监控系统[J].煤炭技术,2010,29(12):139-140.Qin Bangzhen,Qin Jie,Li Aisheng,et al.Main fan remote intrgeated monitoring system is PAC based on coal mine[J].Coal Technology,2010,29(12):139-140.
[10]张卫兵.基于信息融合的液压缸故障模式识别方法研究[D].武汉:武汉理工大学,2012.
[11]吴闪珠.综采液压支架常见故障分析及对策[J].机械工程与自动化,2013(6):186-187.Wu Shanzhu.Fault analysis and countermeasures of full-mechanized hydraulic support[J].Mechanical Engineering&Automation,2013(6):186-187.
[12]薄秀英.矿山机电设备故障诊断技术分析探讨[J].煤炭科学技术,2013,41(S1):138-139.Bo Xiuying.Analysis and discussion on fault diagnosis technical of mining machinery and electrical equipment[J].Coal Science and Technology,2013,41(S1):138-139.
[13]路林旺.在线诊断技术在煤矿大型机电设备中的应用研究[J].煤炭科学技术,2011,39(S1):56-58.Lu Linwang.Application and research on online diagnosis technology in mine large mechanical and electrical equipment[J].Coal Science and Technology,2011,39(S1):56-58.
[14]张登军,薛万安,于永飞.矿山设备故障诊断复合监测技术[J].煤炭科学技术,2012,40(1):111-114.Zhang Dengjun,Xue Wanan,Yu Yongfei.Fault diagnosis as well as monitoring and measuring technology of mine equipment[J].Coal Science and Technology,2012,40(1):111-114.
[15]杨华,林卉.数据融合的研究综述[J].矿山测量,2005(3):24-28.Yang Hua,Lin Hui.Summary of the research on the data fusion[J].Mine Surveying,2005(3):24-28.
[16]杨红军,张遂安,秦菁,等.基于多源信息融合的煤层气水平井随钻判识技术[J].煤炭科学技术,2016,44(5):100-105.Yang Hongjun,Zhang Suian,Qin Jing,et al.Identification technology of coalbed methane horizontal well while drilling based on multi-information integration[J].Coal Science and Technology,2016,44(5):100-105.
[17]王敏,王万俊,熊春山,等.基于多传感器数据融合的故障诊断技术[J].华中科技大学学报:自然科学版,2001,29(2):96-98.Wang Min,Wang Wanjun,Xiong Chunshan,et al.Fault diagnosis technology based on multi-sensor data fusion[J].Journal of Huazhong University of Science and Technology:Nature Science Edition,2001,29(2):96-98.
[18]李然,王伟.综采集成供液系统智能监测诊断技术现状与发展[J].煤炭科学技术,2016,44(3):91-95.Li Ran,Wang Wei.Status and development of intelligent monitoring and diagnosis technology for fully-mechanized integrated pressure pumping system[J].Coal Science and Technology,2016,44(3):91-95.
[19]韩寿松,刘相波,晁智强,等.基于数据融合技术的液压系统故障诊断[J].装甲兵工程学院学报,2005,19(1):57-61.Han Shousong,Liu Xiangbo,Chao Zhiqiang,et al.Fault diagnosis of hydraulic system based on data fusion technology[J].Journal of Academy of Aramorde Force Engineering,2005,19(1):57-61.
[20]张宪宇,陈小虎,何庆飞.基于AMESim的液压缸故障建模与仿真[J].液压气动与密封,2011,31(10):26-28.Zhang Xianyu,Chen Xiaohu,He Qingfei.Modeling and simulation for fault of hydraulic actuator based on AMESim[J].Hydraulics Pneumatics&Seals,2011,31(10):26-28.
[21]周小军,姜乐华,高经纬.基于AMESim的液压缸内泄漏仿真分析[J].机床与液压,2012,40(23):147-150.Zhou Xiaojun,Jiang Lehua,Gao Jingwei.Simulation analysis for inner leakage of hydraulic cylinder based on AMESim[J].Machine Tool&Hydraulics,2012,40(23):147-150.