基于数据融合的动态称重传感器布局研究
|
摘要
随着我国国民经济的迅速发展,我国的交通事业也取得了巨大的进步,公路里程在不断的增加,但不容忽视的是越来越多的超限车辆对公路和桥梁造成了极大的破坏,动态称重是解决车辆超限超载,快速称重的有效手段。现有的动态称重多采用宽幅、单个传感器来完成对于行驶中车辆的轴重获取,进而达到判定是否超载超限的目的。这种方法有很大的弊端,其中最大的问题就是在各种干扰下,测量的精度存在不足。为了克服种种弊端,拟采用多个窄幅传感器,合理布局,达到更高精度的称重目的。
本文主要的研究工作是对动态称重传感器布局的研究,以及数据融合理论对于布局的验证,全文组织如下:
1介绍了超限运输对公路路面,交通安全,运输市场,周边环境,国民经济造成的危害,分析了汽车动态称重系统的组成,并论述了各个不同称重传感器的测量原理及压电传感器的应用目的。对动态称重信号进行分析,并对其中的动态载荷进行归纳,得到利于多传感器布局的模型;
2研究了称重传感器布局的算法,对于时序步进法和数据融合在多传感器布局的应用进行研究。在应用数据融合算法进行布局时,对于不同的布局进行验证,对于运算结果精度的验证采用改进的参数估计法来获取,针对不同的布局,对得到真实轴重的可行性进行对比。
Chinese transport has made great progress with the rapid development of national economy, the mileage of highway increasing constantly, but the breakage of more and more overload vehicles on the roads and bridges can not be ignored. WIM(weigh-in-motion) is the effective means which can solve overloading and transfinite of vehicles, and gain the weight rapidly. The width, single sensor is used in the existing WIM to gain the axle load of the moving vehicle, and then reach the purpose of determine whether the vehicle overloading and transfinite. There are some serious shortcomings of this method, and the biggest problem is the lack of precision in measurement under interference. In order to overcome the drawbacks, we adopted more than one narrow range of sensors and distribute rationally, to achieve high-precision weighing.
In this paper, the research work is that the layout of the WIM sensors, as well as the data fusion theory in the layout verification, the full text is organized as follows:
1 The harm of the transfinite transport are done to the road surface, traffic safety, transport markets, the surrounding environment, and the national economy. The constitute of the vehicle WIM is analyzed, then the measuring principle of different weighing sensor and the purpose of piezoelectric sensor application are discussed. The WIM signal is analyzed, and the dynamic load of which is summarized and then gain the model which in favor of the sensor layout;
2 The algorithm of weighing sensor layout is studied, the application of the timing stepping and data fusion in the layout of multi-sensor are researched. In the application of data fusion algorithm for the layout, the results’accuracy of the calculations is verified by the Parameter estimation which is improved to obtain the verifying of the different layout, for different layouts, the feasibility is contrasted after got the real axle load.
本文主要的研究工作是对动态称重传感器布局的研究,以及数据融合理论对于布局的验证,全文组织如下:
1介绍了超限运输对公路路面,交通安全,运输市场,周边环境,国民经济造成的危害,分析了汽车动态称重系统的组成,并论述了各个不同称重传感器的测量原理及压电传感器的应用目的。对动态称重信号进行分析,并对其中的动态载荷进行归纳,得到利于多传感器布局的模型;
2研究了称重传感器布局的算法,对于时序步进法和数据融合在多传感器布局的应用进行研究。在应用数据融合算法进行布局时,对于不同的布局进行验证,对于运算结果精度的验证采用改进的参数估计法来获取,针对不同的布局,对得到真实轴重的可行性进行对比。
Chinese transport has made great progress with the rapid development of national economy, the mileage of highway increasing constantly, but the breakage of more and more overload vehicles on the roads and bridges can not be ignored. WIM(weigh-in-motion) is the effective means which can solve overloading and transfinite of vehicles, and gain the weight rapidly. The width, single sensor is used in the existing WIM to gain the axle load of the moving vehicle, and then reach the purpose of determine whether the vehicle overloading and transfinite. There are some serious shortcomings of this method, and the biggest problem is the lack of precision in measurement under interference. In order to overcome the drawbacks, we adopted more than one narrow range of sensors and distribute rationally, to achieve high-precision weighing.
In this paper, the research work is that the layout of the WIM sensors, as well as the data fusion theory in the layout verification, the full text is organized as follows:
1 The harm of the transfinite transport are done to the road surface, traffic safety, transport markets, the surrounding environment, and the national economy. The constitute of the vehicle WIM is analyzed, then the measuring principle of different weighing sensor and the purpose of piezoelectric sensor application are discussed. The WIM signal is analyzed, and the dynamic load of which is summarized and then gain the model which in favor of the sensor layout;
2 The algorithm of weighing sensor layout is studied, the application of the timing stepping and data fusion in the layout of multi-sensor are researched. In the application of data fusion algorithm for the layout, the results’accuracy of the calculations is verified by the Parameter estimation which is improved to obtain the verifying of the different layout, for different layouts, the feasibility is contrasted after got the real axle load.
引文
1 Jinfang Wang, Mingguang Wu. An Overview of Research on Weigh-in-motion System. Intelligent Control and Automation. 2004, 6:5241-5244
2贺曙新.车辆动态称重技术的历史、现状与展望.中外公路. 2004, 24(6):104-108
3程路.车辆动态技术研究.浙江大学博士论文. 2008:9-10
4凌杰.公路动态称重系统的设计理论研究.长安大学博士论文. 2001:50~89
5 C. R. Liu, L. Guo, J. Li, X. Chen. Weigh-in-Motion (WIM) Sensor Based on EM Resonant Measurements. Antennas and Propagation International Symposium 2007 IEEE, 2007:561-564
6 Xuemin Chen, Lianhe Guo,Jingyan Yu, Jing Li, Liu R. Evaluating Innovative Sensors and Techniques for Measuring Traffic Loads. Networking. Sensing and Control. 2008:1074-1079
7郑军庭,李建勋,杨恒,李玉芳.基于桥梁应变的高速动态车辆超载检测研究.系统仿真学报. 2007,19(1):197-200
8史航,李霆.基于压电传感器的动态称重系统的分析与设计.华东公路. 2005, 5:87-89
9孙璐,邓学钧.车辆-路面相互作用产生的动力荷载.东南大学学报. 1996, 26(5):142-145
10刘大维,陈静,霍炜,陈秉聪.车辆对路面作用随机动载荷的实验.农业机械学报. 2005, 36(7):12-14
11李维来,潘建军,朱莉.光纤光栅汽车衡动态模型参数估计称重算法.武汉理工大学学报, 2008, 30(2):140-142
12 Jianfeng Li, Lifang Zhou, Qingqing Wang. Study on Dynamic Weighing System base on intelligent algorithm. IEEE International Conference on Control and Automation. 2007:765-768
13 Toshiro ONO. On Dynamic Weighing of Highway Vehicles in Motion. SICE Annual conference, Fukui, 2003:2108-2114
14 George Yannis, Constantinos Antoniou. Integration of Weigh-in-Motion Technologies in Road Infrastructure Management. Institute of Transportation Engineers Journal, 2005, 75(l):39-43
15 Tielking J.T, Roberts F.L, et al. Tire contact pressure and its effete on pavement strain. Journal of Transportation Engineering, 1987, 113(l):56-70
16魏鲁原,伍斌,崔霞.动态称重系统的设计.自动化仪表. 2002, 23(8):34-37
17 Youzhu Ling, Xiaoguang Xu, Lina Shen; Jingmeng Liu. Multi sensor data fusion method based on fuzzy neural network. 6th IEEE International Conference on Industrial Informatics, 13-16 July 2008:153–158
18黎志刚,蔡萍,周志峰.基于BP网络的汽车动态称重数据处理方法,微计算机信息, 2006, 22(8-2):259-261
19 M Mirmomeni, K, Rahmani, C. Lucas. Spacecraft attitude estimation with the aid of Locally Linear Neurofuzzy models and multi sensor data fusion approaches. 4th International Conference on Autonomous Robots and Agents,10-12 Feb. 2009:127– 132
20 Narendra K, Parthasarathy K. Identification and Control of Dynamical Systems Using Neural Networks, IEEE Trans on Neural Networks, 1990, l(l):4-27
21许嘉,蔡萍,周志峰.参数估计在汽车动态称重中的应用,自动化技术与应用, 2005, 24(8):69-71
22 K. Fukuda, H. Tottori, K. Kameoka, T. Ono; K. Yoshida. A method for estimating axle weights of in-motion vehicles and its evaluation. Proceedings of the 41st SICE Annual Conference Volume 2, 5-7 Aug. 2002, vol:1014– 1018
23胡春海,陈静.用于动态称重的模型参数估计方法.仪器仪表装置, 2005,(4):23~25
24 A. Anand Bardwaj, M. Anandaraj, K.Kapil, S. Vasuhi, V.Vaidehi. Multi Sensor Data Fusion Methods Using Sensor Data Compression and Estimated Weights. Signal Processing, International Conference on Communications and Networking, 4-6 Jan. 2008:250–254
25凌杰,龙水根.优化算法在汽车动态称重中的应用.西安公路交通大学学报. 2001, 21(3):77-79
26盖广洪.经验模态分解的一种改进算法.西安交通大学学报. 2004, 38(11):1200~1202
27于哲峰,杨智春. EMD技术在动态称重数据处理中的应用.机械科学与技术. 2003, 23(4):444-446
28周志峰,蔡萍,许嘉,陈日兴.基于经验模态分解的汽车动态称重数据处理,中国机械工程, 2005, 16(22):1813-1816
29 Z.K.Peng, P.W.Tse, F.L.Chu. An improved Hilbert-Huang transform and its application in vibration signal analysis. Journal Sound and Vibration, 2005, 28(6):187-205
30廖庆斌,李舜酩,辛江慧.时序多相关-经验模式分解方法及其对车辆振动信号的分析.南京航空航天大学学报, 2007, 39(4):465-470
31 Ling Yu, Tommy H, T.Chan. Identification of Multi-Axle Vehicle Loads on Bridges. Journal of Vibration and Acoustics. 2004, 126:17-26
32蔡坤,陈惠滨,陈仅星.基于二阶系统的动态汽车衡称重方法及应用.传感技术学报. 2005, 18(4):906-909
33周志峰,蔡萍,陈日兴,黎志刚.基于非线性拟合的汽车动态称重数据处理新方法.上海交通大学学报, 2006, 40(5):719-712
34杨万海.多传感器数据融合及其应用.西安:西安电子科技大学出版社. 2004:10-12
35 Liu Hang, Dou Lihua, Pan Feng, Dong Lingxun. Research on Data association in Three PassiveSensors Network. 2007 IEEE International Conference on Control and Automation:3235-3238
36焦竹青,熊伟丽,张林,徐保国.基于信任度的多传感器数据融合及其应用.东南大学学报. 2008, 38:253-257
37刘建书,李人厚,刘云龙,张贞耀.基于相关性函数和模糊综合函数的多传感器数据融合.系统工程与电子. 2006, 28(7):1006-1009
38 L.D.L. Perera, W.S. Wijesoma, M.D. Adams. On Multidimensional Assignment Data Association for Simultaneous Robot Localization and Mapping. Proceeding of the 2004 IEEE international conference on robotics and automation:860-865
39蔡菲娜,刘勤贤,朱根兴,杜丰.数据融合方法在单传感器系统中的应用.数据采集与处理. 2005, 20(1):88-90
40 M.Jafaripanah, B.M.Al-Hashimi, N.M.White. Load Cell Response Correction Using Analog Adaptive Techniques. Proceedings of 2003 IEEE International Symposium on Circuits and Systems(ISCAS2003)IV:752-755
41王婷杰,施惠昌.一种基于模糊理论的一致性数据融合方法.传感器技术. 1999, 18(16):50-53
42 J. D. Muhs, J. K. Jordan, M. B. Scudiere, K W. Tobin. Results of a portable fiber-optic weigh-in-motion system. Fiber Optic and Laser Sensors. SPIE Vol. 1584. 1991(IX):374-386
43 Lianhe Guo, Yumei Tang, Jingyan Yu, Jing Li, Xuemin Chen, Richard Liu. Weigh-in-motion system design with piezoelectric sensor. Earth and Space, 2004:540-545
44 Wang Jia-fu, et al. Frequency Characteristics of the Input Thresholds of Stochastic Resonant Systems. Acta Phys. Sin, 1997, 46(12):2305-2312
45张宇,赵远,唐勐,吕海峰,孙秀冬.随机共振应用于大频率周期性弱信号检测.哈尔滨工业大学学报. 2008, 40(11):1793-1795
46 Leng Yong-gang, Wang Tai-yong. Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy noise. Acta Phys Sin, 2003, 52(10):2432-2437
47薛履中.工程最优化技术.天津:天津大学出版社. 1988:112-141
2贺曙新.车辆动态称重技术的历史、现状与展望.中外公路. 2004, 24(6):104-108
3程路.车辆动态技术研究.浙江大学博士论文. 2008:9-10
4凌杰.公路动态称重系统的设计理论研究.长安大学博士论文. 2001:50~89
5 C. R. Liu, L. Guo, J. Li, X. Chen. Weigh-in-Motion (WIM) Sensor Based on EM Resonant Measurements. Antennas and Propagation International Symposium 2007 IEEE, 2007:561-564
6 Xuemin Chen, Lianhe Guo,Jingyan Yu, Jing Li, Liu R. Evaluating Innovative Sensors and Techniques for Measuring Traffic Loads. Networking. Sensing and Control. 2008:1074-1079
7郑军庭,李建勋,杨恒,李玉芳.基于桥梁应变的高速动态车辆超载检测研究.系统仿真学报. 2007,19(1):197-200
8史航,李霆.基于压电传感器的动态称重系统的分析与设计.华东公路. 2005, 5:87-89
9孙璐,邓学钧.车辆-路面相互作用产生的动力荷载.东南大学学报. 1996, 26(5):142-145
10刘大维,陈静,霍炜,陈秉聪.车辆对路面作用随机动载荷的实验.农业机械学报. 2005, 36(7):12-14
11李维来,潘建军,朱莉.光纤光栅汽车衡动态模型参数估计称重算法.武汉理工大学学报, 2008, 30(2):140-142
12 Jianfeng Li, Lifang Zhou, Qingqing Wang. Study on Dynamic Weighing System base on intelligent algorithm. IEEE International Conference on Control and Automation. 2007:765-768
13 Toshiro ONO. On Dynamic Weighing of Highway Vehicles in Motion. SICE Annual conference, Fukui, 2003:2108-2114
14 George Yannis, Constantinos Antoniou. Integration of Weigh-in-Motion Technologies in Road Infrastructure Management. Institute of Transportation Engineers Journal, 2005, 75(l):39-43
15 Tielking J.T, Roberts F.L, et al. Tire contact pressure and its effete on pavement strain. Journal of Transportation Engineering, 1987, 113(l):56-70
16魏鲁原,伍斌,崔霞.动态称重系统的设计.自动化仪表. 2002, 23(8):34-37
17 Youzhu Ling, Xiaoguang Xu, Lina Shen; Jingmeng Liu. Multi sensor data fusion method based on fuzzy neural network. 6th IEEE International Conference on Industrial Informatics, 13-16 July 2008:153–158
18黎志刚,蔡萍,周志峰.基于BP网络的汽车动态称重数据处理方法,微计算机信息, 2006, 22(8-2):259-261
19 M Mirmomeni, K, Rahmani, C. Lucas. Spacecraft attitude estimation with the aid of Locally Linear Neurofuzzy models and multi sensor data fusion approaches. 4th International Conference on Autonomous Robots and Agents,10-12 Feb. 2009:127– 132
20 Narendra K, Parthasarathy K. Identification and Control of Dynamical Systems Using Neural Networks, IEEE Trans on Neural Networks, 1990, l(l):4-27
21许嘉,蔡萍,周志峰.参数估计在汽车动态称重中的应用,自动化技术与应用, 2005, 24(8):69-71
22 K. Fukuda, H. Tottori, K. Kameoka, T. Ono; K. Yoshida. A method for estimating axle weights of in-motion vehicles and its evaluation. Proceedings of the 41st SICE Annual Conference Volume 2, 5-7 Aug. 2002, vol:1014– 1018
23胡春海,陈静.用于动态称重的模型参数估计方法.仪器仪表装置, 2005,(4):23~25
24 A. Anand Bardwaj, M. Anandaraj, K.Kapil, S. Vasuhi, V.Vaidehi. Multi Sensor Data Fusion Methods Using Sensor Data Compression and Estimated Weights. Signal Processing, International Conference on Communications and Networking, 4-6 Jan. 2008:250–254
25凌杰,龙水根.优化算法在汽车动态称重中的应用.西安公路交通大学学报. 2001, 21(3):77-79
26盖广洪.经验模态分解的一种改进算法.西安交通大学学报. 2004, 38(11):1200~1202
27于哲峰,杨智春. EMD技术在动态称重数据处理中的应用.机械科学与技术. 2003, 23(4):444-446
28周志峰,蔡萍,许嘉,陈日兴.基于经验模态分解的汽车动态称重数据处理,中国机械工程, 2005, 16(22):1813-1816
29 Z.K.Peng, P.W.Tse, F.L.Chu. An improved Hilbert-Huang transform and its application in vibration signal analysis. Journal Sound and Vibration, 2005, 28(6):187-205
30廖庆斌,李舜酩,辛江慧.时序多相关-经验模式分解方法及其对车辆振动信号的分析.南京航空航天大学学报, 2007, 39(4):465-470
31 Ling Yu, Tommy H, T.Chan. Identification of Multi-Axle Vehicle Loads on Bridges. Journal of Vibration and Acoustics. 2004, 126:17-26
32蔡坤,陈惠滨,陈仅星.基于二阶系统的动态汽车衡称重方法及应用.传感技术学报. 2005, 18(4):906-909
33周志峰,蔡萍,陈日兴,黎志刚.基于非线性拟合的汽车动态称重数据处理新方法.上海交通大学学报, 2006, 40(5):719-712
34杨万海.多传感器数据融合及其应用.西安:西安电子科技大学出版社. 2004:10-12
35 Liu Hang, Dou Lihua, Pan Feng, Dong Lingxun. Research on Data association in Three PassiveSensors Network. 2007 IEEE International Conference on Control and Automation:3235-3238
36焦竹青,熊伟丽,张林,徐保国.基于信任度的多传感器数据融合及其应用.东南大学学报. 2008, 38:253-257
37刘建书,李人厚,刘云龙,张贞耀.基于相关性函数和模糊综合函数的多传感器数据融合.系统工程与电子. 2006, 28(7):1006-1009
38 L.D.L. Perera, W.S. Wijesoma, M.D. Adams. On Multidimensional Assignment Data Association for Simultaneous Robot Localization and Mapping. Proceeding of the 2004 IEEE international conference on robotics and automation:860-865
39蔡菲娜,刘勤贤,朱根兴,杜丰.数据融合方法在单传感器系统中的应用.数据采集与处理. 2005, 20(1):88-90
40 M.Jafaripanah, B.M.Al-Hashimi, N.M.White. Load Cell Response Correction Using Analog Adaptive Techniques. Proceedings of 2003 IEEE International Symposium on Circuits and Systems(ISCAS2003)IV:752-755
41王婷杰,施惠昌.一种基于模糊理论的一致性数据融合方法.传感器技术. 1999, 18(16):50-53
42 J. D. Muhs, J. K. Jordan, M. B. Scudiere, K W. Tobin. Results of a portable fiber-optic weigh-in-motion system. Fiber Optic and Laser Sensors. SPIE Vol. 1584. 1991(IX):374-386
43 Lianhe Guo, Yumei Tang, Jingyan Yu, Jing Li, Xuemin Chen, Richard Liu. Weigh-in-motion system design with piezoelectric sensor. Earth and Space, 2004:540-545
44 Wang Jia-fu, et al. Frequency Characteristics of the Input Thresholds of Stochastic Resonant Systems. Acta Phys. Sin, 1997, 46(12):2305-2312
45张宇,赵远,唐勐,吕海峰,孙秀冬.随机共振应用于大频率周期性弱信号检测.哈尔滨工业大学学报. 2008, 40(11):1793-1795
46 Leng Yong-gang, Wang Tai-yong. Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy noise. Acta Phys Sin, 2003, 52(10):2432-2437
47薛履中.工程最优化技术.天津:天津大学出版社. 1988:112-141