基于动态模态分解的输电杆塔松动检测

摘要
图/表
参考文献(25)
相关文章 (15)

全文: PDF (1830 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为了识别输电杆塔的松动状态，提出分段高阶动态模态分解（SHDMD）的检测方法。为减小振动耦合对检测的影响，利用三轴加速度和三轴角速度构造时间-空间矩阵，从中提取准确的方向振型作为松动特征，首先将时间-空间矩阵在时间维度上划分为若干子矩阵，对每个子矩阵进行空间维度扩展，避免DMD分解时得到错误结果，对扩展后的子矩阵进行DMD分解，得到不同时段的振动模态，利用稳定图筛选出不同时段共有的模态作为真实模态，提取与模态一一对应的方向振型。最后建立灰色关联检测模型，通过计算方向振型的几何特征关联度，识别当前松动状态。模拟杆塔实验与真实杆塔实验结果证明，所提方法能够很好地实现输电杆塔松动位置与松动程度的识别。

	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
	杨金显1
	2
	申刘阳1
	2
	郑泽南1
	2
	李田田1
	2
	杨雨露1
	2

关键词 ：输电杆塔, 结构松动, MEMS, DMD, 稳定图, 灰色关联分析

Abstract：In order to identify the looseness of transmission towers, a segment high-order dynamic mode decomposition (SHDMD) algorithm is proposed. To reduce the impact of vibration coupling, directional mode shapes are extracted from the time-space matrix using three-axis acceleration and three-axis angular velocity as the feature. First, the time-space matrix is divided into several sub-matrices in time dimension, and each sub-matrix is expanded in space dimension to avoid error results from the DMD decomposition. Expanded sub-matrixes in different periods are decomposed by DMD algorithm for obtaining the vibration modes. The common modes in different periods are selected as the real modes by using the stability diagram, and the directional vibration modes corresponding to the modes are extracted. Finally, the grey correlation detection model is established to identify the current looseness state by calculating the geometric feature correlation degree of the directional vibration mode. The results of model and real tower experiments show that the proposed method can well identify the loose position and degree of transmission towers.

Key words： Transmission tower Loose structure MEMS DMD Stability diagram Grey relational analysis

收稿日期: 2022-10-09 出版日期: 2023-10-15

引用本文:

杨金显1,2，申刘阳1,2，郑泽南1,2，李田田1,2，杨雨露1,2. 基于动态模态分解的输电杆塔松动检测[J]. 振动与冲击, 2023, 42(19): 204-211.
YANG Jinxian1,2, SHEN Liuyang1,2, ZHENG Zenan1,2, LI Tiantian1,2, YANG Yulu1,2. Transmission tower looseness detection based on dynamic mode decomposition. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(19): 204-211.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I19/204

[1] 何先龙, 佘天莉, 徐兵, 等. 基于塔筒振动特性识别风机塔螺栓松动的研究[J]. 振动与冲击, 2016, 35(14):112-118.
HE Xian-long, SHE Tian-li, XU Bing, et al. Method for detecting bolts looseness of a wind turbine tower based on its vibration characteristics[J]. Journal of Vibration and Shock. 2016, 35(14):112-118.
[2] Zai B A, Khan M A, Khan K A, et al. The role of dynamic response parameters in damage prediction[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233(13): 4620-4636.
[3] Bao Y, Chen Z, Wei S, et al. The state of the art of data science and engineering in structural health monitoring[J]. Engineering, 2019, 5(2): 234-242.
[4] Hou R, Xia Y. Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019[J]. Journal of Sound and Vibration, 2021, 491: 115741.
[5] 赵超, 赵家钰, 孙清, 等. 环境激励下输电塔动力特性参数识别[J]. 振动与冲击, 2021, 40(4): 30-35.
ZHAO Chao, ZHAO Jiayu, SUN Qing1, et al. A study on identification of dynamic characteristic parameters of a transmissiontower under ambient excitations[J]. Journal of Vibration and Shock, 2021, 40(4): 30-35
[6] 贺光宗, 陈怀海, 孙建勇. 多轴向与单轴向随机激励下结构动力学响应对比研究[J]. 振动与冲击, 2017, 36(14): 194-201.
HE Guangzong, CHEN Huaihai, SUN Jianyong. Dynamic responses of structures under multiaxial and uniaxial random excitations[J]. Journal of Vibration and Shock, 2017, 36(14): 194-201.
[7] 陈岩. 螺栓松动的失效机理以及对整体结构力学行为的影响研究[D]. 大连理工大学, 2019.
Chen Yan. Study on the failure mechanism and the effect on mechanical behavior of the whole structure of bolt loosening[D]. Dalian University of Technology, 2019.
[8] Wang F, Ma J, Kang X, et al. Building response analyses recorded by force-balanced and micro-electro mechanical system accelerometers[J]. Mechanics of Advanced Materials and Structures, 2022, 29(11): 1650-1660.
[9] Chen J G, Wadhwa N, Cha Y J, et al. Modal identification of simple structures with high-speed video using motion magnification[J]. Journal of Sound and Vibration, 2015, 345: 58-71.
[10] Oh B K, Kim D, Park H S. Modal response‐based visual system identification and model updating methods for building structures[J]. Computer‐Aided Civil and Infrastructure Engineering, 2017, 32(1): 34-56.
[11] Bonessio N, Benzoni G, Lomiento G. A multi-mode approach for multi-directional damage detection in frame structures[J]. Engineering Structures, 2017, 147: 505-516.
[12] 卓德兵, 曹晖. 基于小波时频图与轻量级卷积神经网络的螺栓连接损伤识别[J]. 工程力学, 2021, 38(9): 228-238.
ZHUO De-bing, CAO Hui. Damage Identification of Bolt Connections Based On wavelet Time-frequency Diagrams and Lightweight convolutional Neural Networks[J]. Engineering Mechanics, 2021, 38(9): 228-238.
[13] Roveri N, Carcaterra A. Damage detection in structures under traveling loads by Hilbert–Huang transform[J]. Mechanical Systems and Signal Processing, 2012, 28: 128-144.
[14] 周文强, 肖黎, 屈文忠. 基于经验模式分解的框架结构螺栓松动检测实验研究[J].振动与冲击,2016,35(08):201-206.
ZHOU Wen-qiang, XIAO Li, QU Wen-zhong. Detection of bolt looseness in frame structures using empirical mode decomposition[J]. Journal of Vibration and Shock, 2016,35 (08): 201-206.
[15] Guo Y, Zhang Z, Yang W, et al. Early bolt looseness state identification via generalized variational mode decomposition and similarity index[J]. Journal of Mechanical Science and Technology, 2021, 35(3): 861-873.
[16] Dawson S, Hemati M S, Williams M O, et al. Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition[J]. Experiments in Fluids, 2016, 57(3): 1-19.
[17] 党章. 改进的动力学模式分解理论及其在轴承故障诊断中的应用研究[D].武汉科技大学,2020.
DANG Zhang. Improved Dynamic Mode Decomposition Theory and Its Application in Bearing Fault Diagnosis[D]. Wuhan University of Science and Technology,2020.
[18] Dang Z, Lv Y, Li Y, et al. A fault diagnosis method for one-dimensional vibration signal based on multiresolution tlsDMD and approximate entropy[J]. Shock and Vibration, 2019, 2019.
[19] 郑建拥, 魏光辉. 基于多分辨率动态模态分解的电磁信号时频-能量分析[J].系统工程与电子技术,2022,44(05):1468-1474.
ZHENG Jianyong，WEI Guanghui. Time-frequency-energy analysis of electromagnetic signals based on multi-resolution dynamic modal decomposition[J]. Systems Engineering and Electronics, 2022,44(05):1468-1474.
[20] 陈学炳, 张人会, 蒋利杰, 郭广强. 离心泵叶轮内非定常流动的动态模态分解分析[J]. 振动与冲击,2022,41(14):33-40+57.
CHEN Xuebing，ZHANG Ｒenhui，JIANG Lijie，et al. DMD analysis on the unsteady flow in a centrifugal pump impeller[J]. Journal of Vibration and Shock, 2022,41(14):33-40+57.
[21] Le Clainche S, Vega J M. Higher order dynamic mode decomposition[J]. SIAM Journal on Applied Dynamical Systems, 2017, 16(2): 882-925.
[22] Brunton B W, Johnson L A, Ojemann J G, et al. Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition[J]. Journal of neuroscience methods, 2016, 258: 1-15.
[23] He M, Liang P, OBrien E, et al. Continuous Modal Identification and Tracking of a Long-Span Suspension Bridge Using a Robust Mixed-Clustering Method[J]. Journal of Bridge Engineering, 2021, 27(3): 05022001.
[24] 谢延敏. 基于Kriging模型和灰色关联分析的板料成形工艺稳健优化设计研究[D].上海交通大学,2007.
Xie Yanmin. Research on Robust Optimization of Sheet Metal Forming Based on Kriging and Grey Relational Analysis[D]. Shanghai Jiao Tong University, 2007.
[25] Zhao Y, Xiao Y, Wang H, et al. Effect of bolt tightening force on the transmission tower modal parameters and a method for looseness identification[J]. Energy Reports, 2021, 7: 842-849.