基于MBD/SVM车钩分离故障预测的新方法
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摘要
为保障车辆安全运行,需要提高车辆实际运行中车钩防跳机构的运动可靠性来避免发生车钩分离故障。以车钩锁铁达到开锁位为评价指标,运用多体动力学(MBD)仿真技术与支持向量机(SVM)智能监督学习分类相结合的方法,对某型车钩的运动可靠性区间进行预测研究。在RecurDyn多体动力学软件环境中构建车辆实际运行时的车钩装置仿真模型,通过仿真实验模拟车辆运行时车钩内锁铁承受纵向和垂向冲击时的位移响应,反求车钩分离时的纵向、垂向以及二者耦合的临界加速度。仿真结果表明了垂向加速度是车钩分离的敏感因素。通过SVM机器学习方法求解出车钩达到分离条件的加速度界线,为车钩装置设计与运用维护提供了约束条件。研究成果为预测铁路车辆车钩装置等复杂机构的运动可靠性提供了一种新方法。
In order to ensure the safe operation of a vehicle, it is necessary to improve the motion reliability of the coupler anti-jump mechanism in the actual operation of the vehicle to avoid the separation failure of the coupler. The unlocked position of the coupler knuckle lock was taken as an indicator and a prediction study was performed on the motion reliability of a certain type of coupler based on the technique of multi-body dynamics(MBD) simulation combined with the support vector machine(SVM) with intelligent supervised learning classification. The coupler model was built in RecurDyn, software environment, and the displacement responses of the coupler knuckle lock under longitudinal and vertical impact in the vehicle running state were simulated. The reverse solution gives the longitudinal acceleration, vertical acceleration and coupled acceleration of the two directions in the coupler seperation process. The SVM method was used to obtain the acceleration boundary between the coupler unlock zone and safety zone. The simulation results show that vertical acceleration is the sensitive factor for coupler separation. The SVM machine learning method is used to solve the acceleration boundary when the coupler reaches the separation condition, which provides constraints for the design and operation of the coupler device. The research results provide a new method for predicting the motion reliability of complex mechanisms such as the railway vehicle coupler device.
In order to ensure the safe operation of a vehicle, it is necessary to improve the motion reliability of the coupler anti-jump mechanism in the actual operation of the vehicle to avoid the separation failure of the coupler. The unlocked position of the coupler knuckle lock was taken as an indicator and a prediction study was performed on the motion reliability of a certain type of coupler based on the technique of multi-body dynamics(MBD) simulation combined with the support vector machine(SVM) with intelligent supervised learning classification. The coupler model was built in RecurDyn, software environment, and the displacement responses of the coupler knuckle lock under longitudinal and vertical impact in the vehicle running state were simulated. The reverse solution gives the longitudinal acceleration, vertical acceleration and coupled acceleration of the two directions in the coupler seperation process. The SVM method was used to obtain the acceleration boundary between the coupler unlock zone and safety zone. The simulation results show that vertical acceleration is the sensitive factor for coupler separation. The SVM machine learning method is used to solve the acceleration boundary when the coupler reaches the separation condition, which provides constraints for the design and operation of the coupler device. The research results provide a new method for predicting the motion reliability of complex mechanisms such as the railway vehicle coupler device.
引文
[1]QU Baozhang,LIU Xin,LU Bihong.A recurdyn based dynamic simulation method for coupler[J].Key Engineering Materials,2014(579/580):368-372.
[2]王云华,刘立平,师睿鑫,等.关于提高13型上作用车钩防分离能力的研究[J].铁道机车车辆,2014,34(5):91-93.WANG Yunhua,LIU Liping,SHI Ruixin,et al.Research on anti-separation ability improvement for 13-type top operation coupler[J].Railway Locomotive&Car,2014,34(5):91-93.
[3]DAUNYS M,PUTNAITE·D,BAZARAS.Principles for modeling technological processes investigation into the strength and durability of automatic coupler SA-3 in railway carriages[J].Transport,2009,24(2):83-92.
[4]苗伟明.13号车钩故障分析及对策研究[D].大连:大连交通大学,2008.
[5]邵恒,许平,赵士忠,等.动车组密接式车钩动力学性能碰撞试验研究[J].铁道车辆,2017,55(2):5-8.SHAO Heng,XU Ping,ZHAO Shizhong,et al.The collision test and research on dynamics performance of tightlock couplers for multiple units[J].Rolling Stock,2017,55(2):5-8.
[6]MASSA A,STRONATI L,ABOUBAKR A K,et al.Numerical study of the noninertial systems:application to train coupler systems[J].Nonlinear Dynamics,2012,68(1/2):215-233.
[7]唐军.13号车钩防跳装置运行状况分析及方案研究[D].大连:大连交通大学,2010.
[8]LU Bihong,WEI Wenyi,QU Baozhang.How couplerknuckle's surface sangle impact on coupler operation performances[J].Key Engineering Materials,2014,620:306-311.
[9]李锋,汤宝平,郭胤.Laplacian双联最小二乘支持向量机用于早期故障诊断[J].振动与冲击,2017,36(16):85-92.LI Feng,TANG Baoping,GUO Yin.Early fault diagnosis using Laplacian twin least squares support vector machine[J].Journal of Vibration and Shock,2017,36(16):85-92.
[10]王保建,张小丽,傅杨奥骁,等.优化支持向量机及其在智能故障诊断中的应用[J].振动、测试与诊断,2017,37(3):547-552.WANG Baojian,ZHANG Xiaoli,FUYANG Aoxiao,et al.Optimization of support vector machine and its application in intelligent fault diagnosis[J].Journal of Vibration,Measurement&Diagnosis,2017,37(3):547-552.
[11]郝驰宇,冯广斌,闰鹏程,等.基于刚柔耦合的综合传动汇流行星排断齿故障动态特性仿真研究[J].振动与冲击,2018,37(5):248-256.HAO Chiyu,FENG Guangbin,YAN Pengcheng,et al.Effects of broken tooth fault on dynamic characteristics of conflux planetary gear in power shift steering transmission based on rigid-flexible coupled method[J].Journal of Vibration and Shock,2018,37(5):248-256.
[12]张小龙,张氢,秦仙蓉,等.基于ITD复杂度和PSO-SVM的滚动轴承故障诊断[J].振动与冲击,2016,35(24):102-107.ZHANG Xiaolong,ZHANG Qing,QIN Xianrong,et al.Rolling bearing fault diagnosis based on ITD Lempel-Ziv complexity and PSO-SVM[J].Journal of Vibration and Shock,2016,35(24):102-107.
[14]曹愈远,张建,李艳军,等.基于模糊粗糙集和SVM的航空发动机故障诊断[J].振动、测试与诊断,2017,37(1):169-173.CAO Yuyuan,ZHANG Jian,LI Yanjun,et al.Aero-engine fault diagnosis based on fuzzy rough set and SVM[J].Journal of Vibration,Measurement&Diagnosis,2017,37(1):169-173.
[15]QU Baozhang,LIU Yu,LU Bihong et al.Working mechanism of brake unit on railway vehicle and its MBDsimulation experiment[J].Key Engineering Materials,2014(579/580):901-905.
[2]王云华,刘立平,师睿鑫,等.关于提高13型上作用车钩防分离能力的研究[J].铁道机车车辆,2014,34(5):91-93.WANG Yunhua,LIU Liping,SHI Ruixin,et al.Research on anti-separation ability improvement for 13-type top operation coupler[J].Railway Locomotive&Car,2014,34(5):91-93.
[3]DAUNYS M,PUTNAITE·D,BAZARAS.Principles for modeling technological processes investigation into the strength and durability of automatic coupler SA-3 in railway carriages[J].Transport,2009,24(2):83-92.
[4]苗伟明.13号车钩故障分析及对策研究[D].大连:大连交通大学,2008.
[5]邵恒,许平,赵士忠,等.动车组密接式车钩动力学性能碰撞试验研究[J].铁道车辆,2017,55(2):5-8.SHAO Heng,XU Ping,ZHAO Shizhong,et al.The collision test and research on dynamics performance of tightlock couplers for multiple units[J].Rolling Stock,2017,55(2):5-8.
[6]MASSA A,STRONATI L,ABOUBAKR A K,et al.Numerical study of the noninertial systems:application to train coupler systems[J].Nonlinear Dynamics,2012,68(1/2):215-233.
[7]唐军.13号车钩防跳装置运行状况分析及方案研究[D].大连:大连交通大学,2010.
[8]LU Bihong,WEI Wenyi,QU Baozhang.How couplerknuckle's surface sangle impact on coupler operation performances[J].Key Engineering Materials,2014,620:306-311.
[9]李锋,汤宝平,郭胤.Laplacian双联最小二乘支持向量机用于早期故障诊断[J].振动与冲击,2017,36(16):85-92.LI Feng,TANG Baoping,GUO Yin.Early fault diagnosis using Laplacian twin least squares support vector machine[J].Journal of Vibration and Shock,2017,36(16):85-92.
[10]王保建,张小丽,傅杨奥骁,等.优化支持向量机及其在智能故障诊断中的应用[J].振动、测试与诊断,2017,37(3):547-552.WANG Baojian,ZHANG Xiaoli,FUYANG Aoxiao,et al.Optimization of support vector machine and its application in intelligent fault diagnosis[J].Journal of Vibration,Measurement&Diagnosis,2017,37(3):547-552.
[11]郝驰宇,冯广斌,闰鹏程,等.基于刚柔耦合的综合传动汇流行星排断齿故障动态特性仿真研究[J].振动与冲击,2018,37(5):248-256.HAO Chiyu,FENG Guangbin,YAN Pengcheng,et al.Effects of broken tooth fault on dynamic characteristics of conflux planetary gear in power shift steering transmission based on rigid-flexible coupled method[J].Journal of Vibration and Shock,2018,37(5):248-256.
[12]张小龙,张氢,秦仙蓉,等.基于ITD复杂度和PSO-SVM的滚动轴承故障诊断[J].振动与冲击,2016,35(24):102-107.ZHANG Xiaolong,ZHANG Qing,QIN Xianrong,et al.Rolling bearing fault diagnosis based on ITD Lempel-Ziv complexity and PSO-SVM[J].Journal of Vibration and Shock,2016,35(24):102-107.
[14]曹愈远,张建,李艳军,等.基于模糊粗糙集和SVM的航空发动机故障诊断[J].振动、测试与诊断,2017,37(1):169-173.CAO Yuyuan,ZHANG Jian,LI Yanjun,et al.Aero-engine fault diagnosis based on fuzzy rough set and SVM[J].Journal of Vibration,Measurement&Diagnosis,2017,37(1):169-173.
[15]QU Baozhang,LIU Yu,LU Bihong et al.Working mechanism of brake unit on railway vehicle and its MBDsimulation experiment[J].Key Engineering Materials,2014(579/580):901-905.
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