Monitoring of Piezoelectric Impedance for Bolt Loosening using General Regression Neural Network
-
摘要: 压电阻抗法是一种有效的定量化检测局部螺栓预紧力变化的有效方法。然而，实际工程结构使用环境复杂，环境温度的变化同样会引起压电片测量到的阻抗信息的变化。准确的监测螺栓预紧力必须对温度影响进行补偿。机器学习方法近年来受到广泛关注，为此本文提出采用广义回归神经网络对阻抗信息进行温度补偿，进而实现了不同温度下螺栓预紧力的定量化监测。该方法利用少量不同环境温度下的健康状态阻抗实部信息，训练广义回归神经网络，则该网络可以输出任意环境温度下的阻抗实部的预测信息，将该预测数据作为该温度下的基准数据，与实测数据对比计算损伤特征参量，即可实现螺栓预紧力定量化监测的目的。试验研究验证了基于广义回归神经网络方法的有效性，并与常用的阻抗曲线有效频率移动方法进行了对比，表明了该方法的准确性。Abstract: Bolted joints are widely used in engineering structures.In the long-term service, the bolt pre-tightening force is likely to decrease due to the complex loading. This decrease will in turn induces loose connection failure. Piezoelectric impedance method is an effective method to detect the change of local bolt pre-tightening force quantitatively. However, the measured impedance also changes with temperature significantly, which brings great difficulty to the bolt pre-tightening monitoring. Hence, the temperature compensation in the bolt monitoringis necessary. In recent years, the machine learning methods have been received extensive attention. Therefore, the general regression neural network (GRNN) to compensate the temperature effect on the impedanceis proposed. Then the quantitative monitoring of bolt pre-tightening force at different temperatures was achieved. In this method, a small amount of data from different temperature was used to train a GRNN, and then impedance at any temperature can be predicted with GRNN. Furthermore, the predicted impedance is used as the benchmark to calculate the pre-tightening force indicator. Finally, the efficiency and accuracy of the present method are validated by the experimental results and common effective frequency shift method, respectively.
-
表 1 不同工况的实际测量温度
扭距/Nm 温度/℃ 10 29.08 36.36 43.53 50.30 8 28.59 36.78 44.36 50.23 6 30.43 37.97 45.50 49.78 4 30.07 35.53 45.72 49.77 2 28.55 35.06 42.80 48.97 0 28.64 36.73 44.21 50.16 
下载: 导出CSV
-
[1] 杜飞, 徐超. 螺栓连接松动的导波监测技术综述[J]. 宇航总体技术, 2018, 2(4): 13-23DU F, XU C. A review on bolt preload monitoring using guided waves[J]. Astronautical Systems Engineering Technology, 2018, 2(4): 13-23 (in Chinese) [2] RAGHAVAN A C, CESNIK C E S. Review of guided-wave structural health monitoring[J]. The Shock and Vibration Digest, 2007, 39(2): 91-114 doi: 10.1177/0583102406075428 [3] MITRA M, GOPALAKRISHNAN S. Guided wave based structural health monitoring: a review[J]. Smart Materials and Structures, 2016, 25(5): 053001 doi: 10.1088/0964-1726/25/5/053001 [4] ZAGRAI A N, GIURGIUTIU V. Health monitoring of aging aerospace structures using the electromechanical impedance method[C]//Proceedings of SPIE 4702, Smart Nondestructive Evaluation for Health Monitoring of Structural and Biological Systems. San Diego: SPIE, 2002: 4702 [5] SOHN H, FARRAR C, INMAN D. Overview of piezoelectric impedance-based health monitoring and path forward[J]. The Shock and Vibration Digest, 2003, 35(6): 451-463 doi: 10.1177/05831024030356001 [6] NA W S, BAEK J. A review of the piezoelectric electromechanical impedance based structural health monitoring technique for engineering structures[J]. Sensors, 2018, 18(5): 1307 doi: 10.3390/s18051307 [7] ANNAMDAS V G M, SOH C K. Application of electromechanical impedance technique for engineering structures: review and future issues[J]. Journal of Intelligent Material Systems and Structures, 2010, 21(1): 41-59 doi: 10.1177/1045389X09352816 [8] 熊先锋, 杨光瑜, 杨拥民, 等. 压电阻抗技术用于结构健康诊断的一种方法[J]. 传感器技术, 2003, 22(10): 62-64XIONG X F, YANG G Y, YANG Y M, et al. Method of structure health diagnosis using piezoelectric impedance technology[J]. Journal of Transducer Technology, 2003, 22(10): 62-64 (in Chinese) [9] 邵俊华, 王涛, 汪正傲, 等. 基于压电阻抗频率变化的螺栓松动检测技术[J]. 中国机械工程, 2019, 30(12): 1395-1399,1408 doi: 10.3969/j.issn.1004-132X.2019.12.002SHAO J H, WANG T, WANG Z A, et al. Bolt looseness detection using piezoelectric impedance frequency shift method[J]. China Mechanical Engineering, 2019, 30(12): 1395-1399,1408 (in Chinese) doi: 10.3969/j.issn.1004-132X.2019.12.002 [10] 任凯, 张子涵, 杜飞, 等. 基于阻抗法的多螺栓连接预紧扭矩检测试验研究[J]. 动力学与控制学报, 2018, 16(5): 467-472REN K, ZHANG Z H, DU F, et al. Experimental study on multi-bolt connection pre-tightening torque testing based on impedance method[J]. Journal of Dynamics and Control, 2018, 16(5): 467-472 (in Chinese) [11] 杨景文, 朱宏平, 王丹生, 等. 基于EMI损伤检测技术的温度补偿研究[J]. 土木工程与管理学报, 2014, 31(3): 7-11,33 doi: 10.3969/j.issn.2095-0985.2014.03.002YANG J W, ZHU H P, WANG D S, et al. Temperature compensation research of damage detection technology based on EMI method[J]. Journal of Civil Engineering and Management, 2014, 31(3): 7-11,33 (in Chinese) doi: 10.3969/j.issn.2095-0985.2014.03.002 [12] PARK G, KABEYA K, CUDNEY H H, et al. Impedance-based structural health monitoring for temperature varying applications[J]. JSME International Journal Series A Solid Mechanics and Material Engineering, 1999, 42(2): 249-258 doi: 10.1299/jsmea.42.249 [13] WANDOWSKI T, MALINOWSKI P H, OSTACHOWICZ W M. Delamination detection in CFRP panels using EMI method with temperature compensation[J]. Composite Structures, 2016, 151: 99-107 doi: 10.1016/j.compstruct.2016.02.056 [14] WANDOWSKI T, MALINOWSKI P H, OSTACHOWICZ W M. Temperature and damage influence on electromechanical impedance method used for carbon fibre–reinforced polymer panels[J]. Journal of Intelligent Material Systems and Structures, 2017, 28(6): 782-798 doi: 10.1177/1045389X16657423 [15] HUYNH T C, KIM J T. RBFN-based temperature compensation method for impedance monitoring in prestressed tendon anchorage[J]. Structural Control and Health Monitoring, 2018, 25(6): e2173 doi: 10.1002/stc.2173 [16] KIM J T, HUYNH T C, LEE S Y, et al. Compensation of temperature effect on impedance-based damage monitoring in prestressed tendon-anchorage system[C]//Proceedings of the 7th International Conference on Advances in Experimental Structural Engineering. Pavia, 2017: 825-839 [17] 朱宏平, 王丹生, 张俊兵. 基于压电阻抗技术的结构损伤识别基本理论及其应用[J]. 工程力学, 2008, 25(S2): 34-43ZHU H P, WANG D S, ZHANG J B. Theory and application of structure damage detection based on piezoelectric impedance technique[J]. Engineering Mechanics, 2008, 25(S2): 34-43 (in Chinese) [18] BAPTISTA F G, BUDOYA D E, DE ALMEIDA V A D, et al. An experimental study on the effect of temperature on piezoelectric sensors for impedance-based structural health monitoring[J]. Sensors, 2014, 14(1): 1208-1227 doi: 10.3390/s140101208 [19] 周敏, 李世玲. 广义回归神经网络在非线性系统建模中的应用[J]. 计算机测量与控制, 2007, 15(9): 1189-1191 doi: 10.3969/j.issn.1671-4598.2007.09.023ZHOU M, LI S L. Application of GRNN and uniform design to nonlinear system modeling[J]. Computer Measurement & Control, 2007, 15(9): 1189-1191 (in Chinese) doi: 10.3969/j.issn.1671-4598.2007.09.023 [20] SPECHT D F. A general regression neural network[J]. IEEE Transactions on Neural Networks, 1991, 2(6): 568-576 doi: 10.1109/72.97934 -
点击查看大图
图(16) / 表(1)
计量
- 文章访问数: 52
- HTML全文浏览量: 38
- PDF下载量: 7
- 被引次数: 0
