车辆动态称重压电信号的 降噪算法与试验

摘要
图/表
参考文献(21)
相关文章 (8)

全文: PDF (1152 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为提高车辆动态称重压电信号的信噪比，要在奇异谱分析降噪算法中确定合理的嵌入维数与重构阶次。基于算法，提出一种稳定性方法确定嵌入维数，并以累积能量贡献率变化量确定重构阶次，在信号上加入不同方差的高斯白噪声进行实验仿真，验证降噪算法的可行性和有效性，并评价噪声方差对结果的影响。在不同车速下，对五种载重不同的车辆进行工程实测试验。结果表明，车速在，平均称重误差控制在，利用降噪算法处理车辆动态称重压电信号，达到较好的降噪效果，称重精度及稳定性均能满足实际工程要求。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章

关键词 ：动态称重, 压电信号, 嵌入维数, 重构阶次, 降噪算法

Abstract：

In order to increase the signal to noise ratio (SNR) of piezoelectric signals in vehicle weigh-in-motion (WIM), it is necessary to determine reasonable embedding dimension and reconstruction order of the singular spectral analysis (SSA) de-noising algorithm. Here, a new stability method based on Cao algorithm was presented to determine embedding dimension, the variation value of cumulative energy contribution rate was used to determine reconstruction order. Gaussian white noise with different variances was added into Lorenz signal to do simulation, verify the feasibility and validity of SSA de-noising algorithm, and evaluate the effect of noise variance on the results. Five vehicles with different loads and at different speeds were used to perform the actual engineering tests. The results showed that when the vehicle speed is within the range of 10-50km/h, the average WIM error is controlled within the range of 2.72 to 4.72 %, so using SSA de-noising algorithm to process vehicle WIM piezoelectric signals can reach a better de-noising effect, the accuracy and stability of WIM can meet practical engineering requirements.

Key words： weigh-in-motion (WIM) piezoelectric signal embedding dimension reconstruction order SSA de-noising algorithm

收稿日期: 2017-02-06 出版日期: 2018-02-28

引用本文:

刘小锋1，冯志敏1，陈跃华1，张刚1，李宏伟2. 车辆动态称重压电信号的降噪算法与试验[J]. 振动与冲击, 2018, 37(5): 180-187.
LIU Xiao-feng 1 FENG Zhi-min 1 CHEN Yue-hua1 ZHANG Gang 1 LI Hong-wei2. SSA de-noising algorithm and tests for piezoelectric signals of vehicle weigh-in-motion. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(5): 180-187.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I5/180

[1] 周志锋，蔡萍，许嘉，等. 基于经验模态分解的汽车动态称重数据处理[J]. 中国机械工程，2005，16(20) ：1813-1816.
ZHOU Zhi-feng, CAI Ping, XU Jia, et al. Application of empirical mode decomposition in weigh-in-motion of vehicles [J]. China Mechanical Engineering, 2005, 16(20): 1813-1816.
[2] 陶秀，刘礼勇，陶圣. 基于压电薄膜的车辆动态称重系统数据采集及处理[J]. 交通信息与安全，2011，29(2) ：130-132.
TAO Xiu, LIU Li-yong, TAO Sheng. Test data acquisition and processing of a piezo-film based vehicle dynamic weighing system [J]. Journal of Transport Information and Safety, 2011, 29(2): 130-132.
[3] 潘文辉. WIM车辆动态称重系统的设计与实现[D]. 成都：电子科技大学，2014.
PAN Wen-hui. Design and implementation of vehicle weigh-in-motion system [D]. Chengdu: University of Electronic Science and Technology of China, 2014.
[4] 李国芳，王力，龙飞. 基于奇异谱分析的高斯噪声降噪改进算法[J]. 计算机工程与设计，2016，37(8)：2143-2150.
LI Guo-fang, WANG Li, LONG Fei. Gaussian noise de-noising optimal algorithm based on singular spectrum analysis [J]. Computer Engineering and Design, 2016, 37(8): 2143-2150.
[5] 潘若禹，李磊. 基于奇异谱分析的动态称重系统算法研究[J]. 公路交通科技，2010，27(12)：128-133.
PAN Ruo-yu, LI Lei, Study of algorithm for dynamic weigh system based on singular spectrum analysis [J]. Journal of Highway and Transportation Research and Development, 2010, 27(12): 128-133.
[6] 黄必飞，冯志敏，张刚，等. 压电薄膜车辆动态称重系统算法研究[J]. 传感技术学报，2016，29(6)：941-946.
HUANG Bi-fei, FENG Zhi-min, ZHANG Gang, et al. Research on algorithm of piezo-film vehicle weigh-in-motion system [J]. Chinese Journal of Sensors and Actuators, 2016, 29(6): 941-946.
[7] Liangyue CAO. Practical method for determining the minimum embedding dimension of a scalar time series [J]. Physica D, 1997, 110(1997): 43-50.
[8] 郑元庄. 非线性系统时间序列分析方法的研究[D]. 厦门：集美大学，2014.
ZHENG Yuan-zhuang. Research on analysis method of time series for nonlinear system [D]. Xiamen: Ji Mei University, 2014.
[9] 岳顺，李小奇，翟长治. 基于改进Cao算法确定奇异谱嵌入维数及应用[J]. 测绘工程，2015，24(3)：64-68.
YUE Shun, LI Xiao-qi, ZHAI Chang-zhi. Determining the singular spectrum embedding dimension based on an improved cao algorithm [J]. Engineering of Surveying and Mapping, 2015, 24(3): 64-68.
[10] 姚洪兴，姜桂仁，耿霞. 相空间重构中参数确定方法的新探讨[J]. 江苏大学学报自然科学版，2005，26(6A):82-85.
YAO Hong-xing, JIANG Gui-ren, GENG Xian. New method about the parameters within phase space reconstruction [J]. Journal of Jiangsu University(Nature Science Edition), 2005, 26(6A):82-85.
[11] 符五久. 确定相空间重构参数的一种新方法*[J]. 振动与冲击，2010，29(12):190-195.
FU Wu-Jiu. A new method for determining parameters of phase space reconstruction* [J]. Journal of Vibration and Shock, 2010, 29(12): 190-195.
[12] 席剑辉，张蕾，苏荣辉，等. 基于三阶累积量的自适应多变量相空间重构[J]. 上海交通大学学报, 2013, 47(8):1234-1238.
XI Jian-hui, ZHANG Lei, SU Rong-hui, et al. Adaptive phase space reconstruction of multivariate based on third order cumulant [J]. Journal of Shanghai Jiaotong University, 2013, 47(8): 1234-1238.
[13] 王益艳. 基于特征均值的SVD信号去噪算法[J]. 计算机应用与软件，2012，29(5)：121-123.
WANG Yi-yan. Mean value of eigenvalue-based SVD signal de-noising algorithm [J]. Computer Applications and Software, 2012,29(5):121-123.
[14] 王树青，林裕裕，孟元栋，等. 一种基于奇异值分解技术的模型定阶方法[J]. 振动与冲击，2012，31(15)：87-91.
Wang Shu-qing, Lin Yu-yu, Meng Yuan-dong, et al. Model order determination based on singular value decomposition [J]. Journal of Vibration and Shock, 2012, 31(15): 87-91.
[15] 钱征文，程礼，李应红. 利用奇异值分解的信号降噪方法[J]. 振动、测试与诊断，2011，31(4)：459-463.
QIAN Zheng-wen, CHENG Li, LI Ying-hong. Noise reduction method based on singular value decomposition [J]. Journal of Vibration Measurement & Diagnosis, 2011, 31(4): 459-463.
[16] 谭子龙，王雪梅，许哲，等. 基于奇异谱分析(SSA)的弹头激波信号处理方法研究[J]. 科学技术与工程，2014，14(19)：74-79.
TAN Zi-Long, WANG Xue-mei, XU Zhe, et al. The study on projectile shockwave signal processing method based on SSA [J]. Science Technology and Engineering, 2014, 14(19): 74-79.
[17] 卢德林，郭兴明. 基于奇异谱分析的心音信号小波包去噪算法研究[J]. 振动与冲击，2013，32(18)：63-69.
LU De-lin, GUO Xing-ming. Wavelet packet de-nosing algorithm for heart sound signal based on singular spectrum analysis [J]. Journal of Vibration and Shock, 2013, 32(18): 63-69.
[18] Aldrich C, Jemwa G T, Krishnannair S. Multiscale process monitoring with singular spectrum analysis [J]. Stellenbosch University of Stellenbosch , 2010, 40(11): 167-172.
[19] 刘元峰，赵玫. 基于奇异谱分析的混沌序列降噪[J]. 上海交通大学学报，2003，37(5)：778-780.
LIU Yuan-feng, ZHAO Mei. De-noising of chaotic time series based on singular spectrum analysis [J]. Journal of Shanghai Jiaotong University, 2003, 37(5): 778-780.
[20] 李娜，方彦军. 利用关联维数分析机械系统故障信号[J]. 振动与冲击， 2007，26(4)：136-139.
LI Na, FANG Yan-jun. Fault signal analysis of mechanical system based on correlation dimension [J]. Journal of Vibration and Shock, 2007, 26(4): 136-139.
[21] 阳建宏，徐金梧，杨德斌，等. 邻域自适应选取的局部投影非线性降噪方法[J]. 振动与冲击，2006， 25(4)：64-67.
YANG Jian-hong, XU Jin-wu, YANG De-bin, et al. Nolinear noise reduction method by local projection with adaptive neighborhood selection[J]. Journal of Vibration and Shock, 2006, 25(4): 64-67.