Please wait a minute...
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (6): 1108-1117    DOI: 10.3785/j.issn.1008-973X.2021.06.011
    
Solving data length limit of all-phase technology by AR model
Xiang-yu GAO(),Yang-long LI()
Key Laboratory of Urban Security and Disaster Engineering, Beijing University of Technology, Beijing 100124, China
Download: HTML     PDF(1878KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A data extrapolation method (AREX) based on autoregressive (AR) model was proposed aiming at the limitation of data length in all-phase technology in the field of building structure. An algorithm for determining the order of AR model suitable for all-phase technology was given. An algorithm based on the principle of energy concentration criterion (ECC) was proposed to determine the order of AR model . The algorithm was compared with the final prediction error (FPE), Akaike's information criterion (AIC), Bayesian information criterion (BIC) and difference spectrum theory of singular value (SVD). Results showed that ECC algorithm was more suitable for AREX method. The original signal and the estimation signal by AREX method and the other expansion method were processed in all-phase technology. Results showed that the similarity of the waveform and spectrum of the AREX estimated signal was better than that of other methods. AREX was applied to the all-phase data processing of actual finite element and shaking table test signals in the field of building structure. Results show that the AREX estimation signal can basically be equivalent to results of the original signal, indicating that AREX can solve the limitation of all-phase data length.



Key wordsall-phase technology      data expansion      autoregressive model      order determination      frequency analysis     
Received: 25 May 2020      Published: 30 July 2021
CLC:  TU 317  
  TN 911  
Fund:  北京市自然科学基金资助项目(8141001)
Cite this article:

Xiang-yu GAO,Yang-long LI. Solving data length limit of all-phase technology by AR model. Journal of ZheJiang University (Engineering Science), 2021, 55(6): 1108-1117.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.06.011     OR     https://www.zjujournals.com/eng/Y2021/V55/I6/1108


利用AR模型解决全相位技术数据长度限制

针对在建筑结构领域全相位技术中对数据长度的限制问题,提出使用基于自回归(AR)模型的数据外推方法(AREX),给出适用于全相位技术的AR模型定阶算法. 提出以有效频段最大能量集中为原则确定AR模型阶数的算法(ECC),与最终预测误差(FPE)、阿凯克信息准则(AIC)、贝叶斯信息准则(BIC)及奇异值差分谱准则(SVD)进行对比. 结果表明,ECC算法更适合AREX方法. 将原始信号及AREX方法与常见数据扩展方法的估计信号进行全相位处理,结果表明,AREX估计信号的波形和频谱的相似程度均优于其余方法. 将AREX方法用于建筑结构有限元分析数据和振动台试验信号的全相位数据处理,结果表明,AREX估计信号基本上可以等效原始信号结果,表明AREX可以解决全相位数据长度的限制问题.


关键词: 全相位技术,  数据扩展,  自回归模型,  定阶准则,  频率分析 
参数 取值 参数 取值
A1 3 ${\phi _1}$ /rad ${\text{π}} /2$
A2 1 ${\phi _2}$ /rad ${\text{π}} /3$
A3 2 ${\phi _3}$ /rad ${\text{π}} /4$
f1 /Hz 12 fs /Hz 256
f2 /Hz 17 N 256
f3 /Hz 21 ? ?
Tab.1 Values of construction parameter
Fig.1 Waveforms of AREX extended data under different order criteria
Fig.2 ApFFT amplitude spectrum of AREX extended data under different fixed order criteria
Fig.3 Correlation coefficients of AREX waveforms with different order determination methods
主频 Acc /%
OPT SVD ECC FPE AIC BIC
1阶 85 35 93 77 78 76
2阶 77 19 84 69 71 69
3阶 100 93 100 96 96 96
Tab.2 Correct rate of apFFT frequency recognition with different order determination methods
Fig.4 Relationship between distribution law of optimal order and SNR
Fig.5 Relationship between distribution law of ECC order and SNR
Fig.6 All-phase waveforms of different expansion methods
Fig.7 ApFFT amplitude spectrum of different expansion methods
Fig.8 Correlation coefficients of all-phase waveforms with different expansion methods
主频 Acc /%
原始信号 DSAF ZP AREX
1阶 100 100 88 94
2阶 83 56 6 85
3阶 42 54 19 95
Tab.3 Correct rate of apFFT frequency recognition with different expansion methods
Fig.9 Finite element model of industrial plant structure
Fig.10 Structural free attenuation signal
Fig.11 Comparison of apFFT analysis results of free attenuation signals
Fig.12 Shaking table structure test model
Fig.13 White noise signal of shaking table structure
Fig.14 Comparison of apFFT analysis results of shaking table signals
[1]   侯正信, 王兆华, 杨喜 全相位DFT数字滤波器的设计与实现[J]. 电子学报, 2003, 31 (4): 539- 543
HOU Zheng-xin, WANG Zhao-hua, YANG Xi Design and implementation of all phase DFT digital filter[J]. Acta Electronica Sinica, 2003, 31 (4): 539- 543
doi: 10.3321/j.issn:0372-2112.2003.04.014
[2]   BU Z, CHANG X, ZHENG Z, et al. High-precision time interval measurement based on triggerable ring oscillators and all-phase FFT algorithm[C]// 2019 Joint Conference of the IEEE International Frequency Control Symposium and European Frequency and Time Forum. Orlando: IEEE, 2019: 1-2.
[3]   孙曙光, 田朋, 杜太行, 等 基于apFFT-AMD的密集频率谐波/间谐波检测[J]. 浙江大学学报: 工学版, 2020, 54 (1): 178- 188
SUN Shu-guang, TIAN Peng, DU Tai-hang, et al Dense frequency harmonic/interharmonic detection based on apFFT-AMD[J]. Journal of Zhejiang University: Engineering Science, 2020, 54 (1): 178- 188
[4]   PAN Y, ZHANG T, ZHANG G, et al A novel acquisition algorithm based on PMF-apFFT for BOC modulated signals[J]. IEEE Access, 2019, 7: 46686- 46694
doi: 10.1109/ACCESS.2019.2909787
[5]   LIU S, LYU N, CUI J, et al Improved blind timing skew estimation based on spectrum sparsity and ApFFT in time-interleaved ADCs[J]. IEEE Transactions on Instrumentation and Measurement, 2019, 68 (1): 73- 86
doi: 10.1109/TIM.2018.2834080
[6]   NGAMKHANONG C, KAEWUNRUEN S The effect of ground borne vibrations from high speed train on overhead line equipment (OHLE) structure considering soil-structure interaction[J]. Science of the Total Environment, 2018, 627: 934- 941
doi: 10.1016/j.scitotenv.2018.01.298
[7]   PAZZI V, LOTTI A, CHIARA P, et al Monitoring of the vibration induced on the arno masonry embankment wall by the conservation works after the May 25, 2016 Riverbank Landslide[J]. Geoenvironmental Disasters, 2017, 4 (1): 6
doi: 10.1186/s40677-017-0072-2
[8]   LIN X, KATO M, ZHANG L, et al Quantitative investigation on collapse margin of steel high-rise buildings subjected to extremely severe earthquakes[J]. Earthquake Engineering and Engineering Vibration, 2018, 17 (3): 445- 457
doi: 10.1007/s11803-018-0454-9
[9]   SACCHI M D, VELIS D R, COMÍNGUEZ A H Minimum entropy deconvolution with frequency-domain constraints[J]. Geophysics, 1994, 59 (6): 938- 945
doi: 10.1190/1.1443653
[10]   WU Z Z, LI Y D, CHANG T Uniqueness theorems and algorithm for discrete signal reconstruction from its autocorrelation function and one sample[J]. Science in China Series A-Mathematics, Physics, Astronomy and Technological Science, 1989, 32 (5): 607- 618
[11]   蔡正辉, 刘怀山, 黄云笛, 等. 地震信号的最小相位重构研究[C]// 第11届国家安全地球物理专题研讨会. 西安: 中国学术期刊电子出版社, 2015: 317–321.
CAI Zheng-hui, LIU Huai-shan, HUANG Yun-di, et al. Reconstruction of minimum phase from seismic data[C]// The 11th National Security Geophysics Symposium. Xi'an: China Academic Journal Electronic Publishing House, 2015: 317–321.
[12]   NGUYEN V K, TURLEY M D E, FABRIZIO G A A new data extrapolation approach based on spectral partitioning[J]. IEEE Signal Processing Letters, 2016, 23 (4): 454- 458
doi: 10.1109/LSP.2016.2533602
[13]   ZHOU J, ZHU Z, SHU Y, et al Extrapolation of RF echo data based on AR modeling[J]. Journal of Nanjing University of Aeronautics and Astronautics, 1999, 16 (2): 193- 199
[14]   沈慧芳, 李民生, 罗丰 基于递推算法的严格最大熵谱估计[J]. 雷达科学与技术, 2008, 6 (4): 288- 291
SHEN Hui-fang, LI Min-sheng, LUO Feng Strict maximum entropy spectral estimation based on recursive algorithm[J]. Radar Science and Technology, 2008, 6 (4): 288- 291
doi: 10.3969/j.issn.1672-2337.2008.04.011
[15]   JIANG Y, HUANG Y, YE X. A new singular value decomposition method for AR model order selection via vibration signal analysis[C]// 6th International Conference on Fuzzy Systems and Knowledge Discovery. Tianjin: IEEE, 2009: 567–572.
[16]   张景润, 李伟光, 李振, 等 基于奇异值差分谱理论的大型转子轴心轨迹提纯[J]. 振动与冲击, 2019, 38 (4): 199- 205
ZHANG Jing-run, LI Wei-guang, LI Zhen, et al Purification for a large rotor axis's orbit based on the difference spectrum theory of singular value[J]. Journal of Vibration and Shock, 2019, 38 (4): 199- 205
[17]   高向宇, 李建勤, 刘超, 等 钢支撑-混凝土框架动力非弹性扭转机理与抗扭设计研究[J]. 建筑结构学报, 2018, 39 (2): 44- 53
GAO Xiang-yu, LI Jian-qin, LIU Chao, et al Mechanism of dynamic inelastic torsion and anti-torsional design strategy of steel-braced concrete frames[J]. Journal of Building Structures, 2018, 39 (2): 44- 53
[1] Ping WANG,Le LIU,Chen-yang HU,Zheng GONG,Jing-mang XU,Zhi-xin WANG. Identification of switch rail brakeage in high speed railway turnout based on elastic wave propagation[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 2038-2046.
[2] TONG Shui-guang, ZHANG Yi-dong, XU Jian, CONG Fei-yun. Spectral band refined composite multiscale fuzzy entropy and its application in fault diagnosis of rolling bearings[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(8): 1509-1516.
[3] ZHENG Xu, HAO Zhi-yong, JIN Yang, LU Zhao-gang. Studying noise contributions of IC engine via MEEMD method[J]. Journal of ZheJiang University (Engineering Science), 2012, 46(5): 954-960.