|
|
Guided wave monitoring method based on over-determined independent component analysisunder temperature variation conditions |
XIAO Hang, XIAO Li,QU Wenzhong |
Department of Engineering Mechanics, Wuhan University, Wuhan 430072, China |
|
|
Abstract Ultrasonic guided wave based structural health monitoring technology will be affected by changing environment and operating conditions in practical engineering applications. Because of the limitations of independent component analysis methods for processing a large number of monitoring guided waves, and the lack of research on different degrees of damage characterization, a guided wave monitoring method based on over-determined independent component analysis is proposed and the damage index based on k-means clustering is improved. The temperature, which has a large influence on the monitoring signal and is widely exist, is taken as an environmental variable. The principal component analysis (PCA) is used to analyze the observation matrix composed of a large number of guided wave signals to determine the number of independent components, and independent component analysis (ICA) is used to decompose the processed guided wave signals into independent components. The influence of damage and that of environmental and operational conditions are separated into different independent components. The guided wave monitoring experiment was carried out on aluminum plates subjected to long-term changes in ambient temperature. The results show that the method can effectively reduce the number of independent components and eliminate the interference from the ambient temperature in a large number of guided signals to identify the damage. Furthermore, the damage identification experiment was carried out on the aluminum plate with intact and different damage degree under the temperature change condition. The effect of the method on charactering the damage degree while eliminating the environmental temperature interference was studied.
|
Received: 13 March 2019
Published: 28 July 2020
|
|
|
|
[1] Ben, B. S., B.A. Ben, Vikram K A, et al. Damage identification in composite materials using ultrasonic based Lamb wave method[J]. Measurement Journal of the International Measurement Confederation, 2013, 46(2):904-912.
[2] Sriramadasu R C, Banerjee S, Lu Y. Detection and Assessment of Pitting Corrosion in Rebars Using Scattering of Ultrasonic Guided Waves[J]. NDT & E International, 2018, 101:53-61.
[3] Mehmet K. Yücel, Legg M, Kappatos V, et al. An ultrasonic guided wave approach for the inspection of overhead transmission line cables[J]. Applied Acoustics, 2017, 122:23-34.
[4] Sohn, H. Effects of environmental and operational variability on structural health monitoring[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2007, 365(1851):539-560.
[5] 邓菲, 刘洋, 诸葛霞, 等. 变化环境下的超声导波结构健康监测研究进展[J]. 机械工程学报, 2016, 52(18):1-7.
DENG Fei, LIU Yang, ZHUGE Xia, et al. Progress on the Research of Ultrasonic Guided Wave Structural Health Monitoring in the Changing Ambient [J]. Journal of Mechanical Engineering, 2016, 52(18):1-7.
[6] Konstantinidis G, Drinkwater B W, Wilcox P D. The temperature stability of guided wave structural health monitoring systems[J]. Smart Materials & Structures, 2006, 15(4):967.
[7] Clarke T, Simonetti F, Cawley P. Guided wave health monitoring of complex structures by sparse array systems: Influence of temperature changes on performance[J]. Journal of Sound & Vibration, 2010, 329(12):2306-2322.
[8] Croxford A J, Moll J, Wilcox P D, et al. Efficient temperature compensation strategies for guided wave structural health monitoring[J]. Ultrasonics, 2010, 50(4):517-528.
[9] 屈文忠, Inman D J. Lamb波结构损伤检测温度补偿方法仿真与实验研究[J]. 振动工程学报, 2013, 26(3): 343-350.
QU Wen-zhong, Inman D J. Experiment and simulation of compensation for temperature influence on Lamb wave propagation for damage detection[J]. Journal of Vibration Engineering, 2013, 26(3): 343-350.
[10] Tibaduiza D A, Mujica L E, Rodellar J. Damage classification in structural health monitoring using principal component analysis and self-organizing maps[J]. Structural Control & Health Monitoring, 2013, 20(10):1303-1316.
[11] Liu C, Harley J B, Bergés M, et al. Robust ultrasonic damage detection under complex environmental conditions using singular value decomposition[J]. Ultrasonics, 2015, 58(55):75-86.
[12] Eybpoosh M, Berges M, Noh H Y. Nonlinear feature extraction methods for removing temperature effects in multi-mode guided-waves in pipes[J]. Proceedings of SPIE - The International Society for Optical Engineering, 2015, 9437:94371W
[13] Eybpoosh M, Berges M, Noh H Y. Sparse representation of ultrasonic guided-waves for robust damage detection in pipelines under varying environmental and operational conditions[J]. Structural Control and Health Monitoring, 2016, 23(2):369-391.
[14] Deraemaeker A, Worden K. A comparison of linear approaches to filter out environmental effects in structural health monitoring[J]. Mechanical Systems and Signal Processing, 2018, 105:1-15.
[15] 李伟, 伍建军, 张鹏飞, 等. 基于独立成分分析的尖轨导波监测信号处理方法研究[J].结构工程师, 2018, 34(5):74-79.
LI Wei, WU Jian-jun ZHANG Peng-fei, et al. Research of guided wave monitoring signal processing for switch rails based on independent component analysis [J]. Structural Engineers, 2018, 34(5):74-79.
[16] 王志阳, 杜文辽, 陈进. 基于模型的CICA及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2015, 34(8):66-70.
WANG Zhi-yang, DU Wen-liao, CHEN Jin. Fault diagnosis of rolling element bearings with model-based constrained independent component analysis [J]. Journal of Vibration and Shock, 2015, 34(8):66-70.
[17] Dobson J, Cawley P. Independent Component Analysis for Improved Defect Detection in Guided Wave Monitoring[J]. Proceedings of the IEEE, 2015, 104(8):1-12.
[18] Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis[J]. IEEE Transactions on Neural Networks, 1999, 10(3):626-634.
[19] Hervé Abdi, Williams Lynne. Principal component analysis[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4):433-459.
[20] 朱孝龙, 张贤达. 基于奇异值分解的超定盲信号分离[J]. 电子与信息学报, 2004, 26(3):337-343.
ZHU Xiao-long, ZHANG Xian-da. Overdetermined blind source separation based on singular value decomposition [J]. Journal of Electronics & Information Technology, 2004, 26(3):337-343.
|
|
|
|