电力液压块式制动器多状态评估模型研究
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摘要
电力液压块式制动器普遍应用于采矿业、港口码头、建筑工地等施工设备的机械制动,对其进行状态评估可以有效地避免故障事故,减少维修费用,延长设备寿命。针对传统状态评估模型建模复杂、识别精度低、实时性差等问题,运用MSET技术构建电力液压块式制动器多状态评估模型。通过分析制动器故障原因和结构原理,选择动摩擦因数、制动油油温、弹簧弹性系数、瓦闸间距、制动接触面积5个参数的状态分量,以其历史正常运行数据为基础建立记忆矩阵D,引用非线性运算子解决矩阵计算中的可逆问题,引用滑动窗口残差统计法消除制动器运行中可能存在的不确定性因素和随机干扰。实验结果表明,该方法能准确有效地对电力液压块式制动器进行状态评估,可用于电力液压块式制动器的预防性维修。
Because the electric hydraulic block brakes are widely used for mechanical braking of construction equipment such as mining industry, ports and docks and construction sites, state evaluation can effectively avoid failure accidents, reduce maintenance costs and prolong the service life of equipment. Considering problems, such as complex modeling, low recognition accuracy and poor real-time performance of traditional state evaluation model, a multi-state evaluation model of electro-hydraulic block brake is built by using MSET technology. By analyzing the cause of brake failure and its structural principle, the state components of five parameters, namely, dynamic friction factor, brake oil temperature, spring elastic coefficient, pad-brake spacing and brake contact area, are selected. Based on its historical normal operation data, a memory matrix D is established. Nonlinear operators are used to solve reversible problems in matrix calculation. Sliding window residual statistics method is used to eliminate possible uncertain factors and random interference in brake operation. Experimental results show that the method can evaluate the state of the electric hydraulic block brake accurately and effectively and can be used for preventive maintenance of the electric hydraulic block brake.
Because the electric hydraulic block brakes are widely used for mechanical braking of construction equipment such as mining industry, ports and docks and construction sites, state evaluation can effectively avoid failure accidents, reduce maintenance costs and prolong the service life of equipment. Considering problems, such as complex modeling, low recognition accuracy and poor real-time performance of traditional state evaluation model, a multi-state evaluation model of electro-hydraulic block brake is built by using MSET technology. By analyzing the cause of brake failure and its structural principle, the state components of five parameters, namely, dynamic friction factor, brake oil temperature, spring elastic coefficient, pad-brake spacing and brake contact area, are selected. Based on its historical normal operation data, a memory matrix D is established. Nonlinear operators are used to solve reversible problems in matrix calculation. Sliding window residual statistics method is used to eliminate possible uncertain factors and random interference in brake operation. Experimental results show that the method can evaluate the state of the electric hydraulic block brake accurately and effectively and can be used for preventive maintenance of the electric hydraulic block brake.
引文
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[3]Z Hameed,Y S Hong,Y M Cho,et al.Condition Monitoring and Fault Detection of Wind Turbines and Related Algorithms:A review[J].Renewable and Sustainable Energy Review(S1364-0321),2009,13(1):1-39.
[4]姚良,李艾华,孙红辉,等.基于MSET和SPRT的内燃机气阀机构振动监测[J].振动工程学报,2009,(2):150-155.
[5]李大中,常永亮,赵杰,等.基于MSET模型的风力发电机故障预警[J].华北电力技术,2016(12):43-48.
[6]Christopher L Black,Robert E Uhrig,Hines J Wesldy.System modeling and instrument calibration verification with an online arstate estimation technique[A].Proceedings of the Main-tenance and Reliability Conference[C].Knoxville,TN,May,1998.
[7]Niu G,Zhao Y,Defoort M,et al.Fault diagnosis of locomotive electro-pneumatic brake through uncertain bond graphmodeling and robust online monitoring[J].Mechanical Systems&Signal Processing,2015,50-51:676-691.
[8]王广铨.工程数据处理[M].长春:吉林大学出版社,1990.
[9]关守平,吕欣.基于过程神经网络的稀疏数据过程建模方法[J].系统仿真学报,2008,20(16):2 893-2 894.
[10]郭鹏,David Infield,杨锡运.风电机组齿轮箱温度趋势状态监测及分析方法[J].中国电机工程学报,2011,31(32):134,135.
[11]冯敏.矿井提升机盘式制动器失效原因分析及解决方案研究[D].长沙:中南大学,2012.
[12]何常远.基于LM-BP神经网络的制动器故障识别系统[A].北京力学会.北京力学会第二十三届学术年会会议论文集[C].北京力学会,2017(6).
[13]陆廷海,张西娥.油温变化对液压泵出口压力的影响[J].林业机械,1981(1)