A multi-sensor based crack propagation monitoring research

CHANG Qi; YANG Weixi; ZHAO Heng; MENG Yao; LIU Jun; GAO Heming

doi:10.7527/S1000-6893.2019.23336

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2020 , Vol. 41 >Issue 2: 223336 - 223336

DOI: https://doi.org/10.7527/S1000-6893.2019.23336

Solid Mechanics and Vehicle Conceptual Design

A multi-sensor based crack propagation monitoring research

CHANG Qi ,
YANG Weixi ,
ZHAO Heng ,
MENG Yao ,
LIU Jun ,
GAO Heming

Expand

School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China

Received date: 2019-08-02

Revised date: 2019-09-09

Online published: 2020-03-03

Supported by

Natural Science Basic Research Program of Shaanxi Province (2018JM5112); Special Scientific Research Project of Shaanxi Province Education Department (15JK1496); National Natural Science Foundation of China (51406164,51775429)

Fold

Abstract

Fatigue crack propagation monitoring research is one of the main problems of structural health monitoring. In order to ensure the reliable and safe operation of metal structures, it is necessary to monitor the fatigue crack growth process of structures in real time. Aiming at the problem of structural crack propagation, this paper adopts two sensors, PieZoelectric Transducers (PZT) and resistance strain gauge, and proposes a comprehensive monitoring method to combine crack monitoring with passive monitoring method for continuous monitoring of structural damage and active monitoring method sensitive to small damage, so as to improve the monitoring level of crack propagation. In this paper, the random forest algorithm is used to identify the crack length, and the D-S evidence theory is used to fuse the recognition results of the two sensors. The crack propagation recognition result is more accurate and reliable than the single sensor. In this paper, the experimental study on crack propagation monitoring based on strain and active Lamb wave is carried out. verifying the effectiveness and practicability of the method for improving the accuracy of crack propagation monitoring and identification.

Key words： fatigue crack propagation; Lamb wave; Random Forest (RF); data fusion; D-S evidence theory

Cite this article

CHANG Qi , YANG Weixi , ZHAO Heng , MENG Yao , LIU Jun , GAO Heming . A multi-sensor based crack propagation monitoring research[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020 , 41(2) : 223336 -223336 . DOI: 10.7527/S1000-6893.2019.23336

References

[1] ANGULO Á, ALLWRIGHT J, MARES C, et al. Finite element analysis of crack growth for structural health monitoring of mooring chains using ultrasonic guided waves and acoustic emission[J]. Procedia Structural Integrity, 2017, 5:217-224.
[2] 杨伟博, 袁慎芳, 邱雷. 基于Lamb波的平尾大轴裂纹扩展监测[J].振动·测试与诊断, 2018, 38(1):143-147, 211-212. YANG W B,YUAN S F,QIU L. Crack growth monitoring of horizontal stabilizer shaft based on Lamb wave[J]. Journal of Vibration,Measurement & Diagnosis, 2018, 38(1):143-147, 211-212(in Chinese).
[3] 李政鸿, 徐武, 张晓晶, 等. 多孔多裂纹平板的疲劳裂纹扩展试验与分析方法[J]. 航空学报, 2018, 39(7):221867. LI Z H, XU W, ZHANG X J, et al. Experimental and analytical analyses of fatigue crack growth in sheets with multiple holes and cracks[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(7):221867(in Chinese).
[4] 赵晓辰, 吴学仁, 童第华, 等. 无限板孔边裂纹问题的高精度解析权函数解[J]. 航空学报, 2018, 39(9):221976. ZHAO X C,WU X R, DONG D H, et al. Accurate analytical weight function solutions for crack at edge of circular hole in infinite plate[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(9):221976(in Chinese).
[5] MASSEREY B, FROMME P. Analysis of high frequency guided wave scattering at a fastener hole with a view to fatigue crack detection[J]. Ultrasonics, 2017, 76:78-86.
[6] CHO H, LISSENDEN C J. Structural health monitoring of fatigue crack growth in plate structures with ultrasonic guided waves[J]. Structural Health Monitoring, 2012, 11(4):393-404.
[7] VERSTRYNGE E, DE WILDER K, DROUGKAS A, et al. Crack monitoring in historical masonry with distributed strain and acoustic emission sensing techniques[J]. Construction and Building Materials, 2018, 162:898-907.
[8] 王建国, 李璐, 王连庆, 等. Ⅰ-Ⅲ型复合加载下铝合金疲劳裂纹扩展速率[J]. 北京科技大学学报, 2011, 33(6):734-738. WANG J G, LI L, WANG L Q, et al. Fatigue crack growth rate of aluminum alloys under Ⅰ-Ⅲ combined loading[J]. Journal of University of Science and Technology Beijing, 2011, 33(6):734-738(in Chinese).
[9] COLOMBO C, DU Y, JAMES M N, et al. On crack tip shielding due to plasticity-induced closure during an overload[J]. Fatigue & Fracture of Engineering Materials & Structures, 2010, 33(12):766-777.
[10] SALVATI E, O'CONNOR S, SUI T, et al. A study of overload effect on fatigue crack propagation using EBSD, FIBDIC and FEM methods[J]. Engineering Fracture Mechanics, 2016, 167:210-223.
[11] 陈健, 袁慎芳, 王卉, 等. 基于高斯权值-混合建议分布粒子滤波的疲劳裂纹扩展预测[J]. 航空学报, 2017, 38(11):220925. CHEN J, YUAN S F, WANG H, et al. Using Gaussian weighting-mixture proposal distribution particle filter for fatigue crack growth prediction[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(11):220925(in Chinese).
[12] CHAPETTI M D, STEIMBREGER C. A simple fracture mechanics estimation of the fatigue endurance of welded joints[J]. International Journal of Fatigue, 2019, 125:23-34.
[13] 柴国钟, 吕君, 鲍雨梅, 等. 表面裂纹疲劳扩展和寿命计算的高效高精度数值分析方法[J]. 航空学报, 2017, 38(12):221291. CHAI G Z, LV J, BAO Y M, et al. A highly efficient and accurate numerical analysis method for fatigue propagation of surface crack and life prediction[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(12):221291(in Chinese).
[14] CAI J, YUAN S, QING X P, et al. Linearly dispersive signal construction of Lamb waves with measured relative wavenumber curves[J]. Sensors & Actuators A Physical, 2015, 221:41-52.
[15] 蒋黎星, 侯进. 基于集成分类算法的自动图像标注[J]. 自动化学报, 2012, 38(8):1257-1262. JIANG L X, HOU J. Image annotation using the ensemble learning[J]. Acta Automatica Sinica, 2012, 38(8):1257-1262(in Chinese).
[16] LUO C, WANG Z, WANG S, et al. Locating facial landmarks using probabilistic random forest[J]. IEEE Signal Processing Letters, 2015, 22(12):2324-2328.
[17] 魏静明, 李应. 利用抗噪纹理特征的快速鸟鸣声识别[J]. 电子学报, 2015, 43(1):185-190. WEI J M, LI Y. Rapid bird sound recognition using anti-noise texture features[J]. Acta Electronica Sinica, 2015, 43(1):185-190(in Chinese).
[18] 王晓军, 袁平, 毛志忠, 等. 基于随机森林的风洞马赫数预测模型[J]. 航空学报, 2016, 37(5):1494-1505. WANG X J, YUAN P, MAO Z Z, et al. Wind tunnel Mach number prediction model based on random forest[J]. Acta Aeronautica et Astronautica Sinica, 2016, 37(5):1494-1505(in Chinese).
[19] GENUER R, POGGI J-M, TULEAU-MALOT C, et al. Random forests for big data[J]. Big Data Research,2017,9:28-46.
[20] PING W, ZHU X. Infer the fact of a case with D-S evidence theory[C]//2009 ETP/IITA World Congress in Applied Computing, Computer Science, and Computer Engineering, 2009:299-302.
[21] 蒋雯, 吴翠翠,贾佳,等.D-S证据理论中的基本概率赋值转换概率方法研究[J].西北工业大学学报,2013,31(2):295-299. JIANG W, WU C C, JIA J, et al. A probabilistic transformation of basic probability assignment (BPA) in D-S evidence theory[J]. Journal of Northwestern Polytechnical University, 2013, 31(2):295-299.
[22] 井立, 杨智春, 张甲奇. 基于信息融合技术的结构损伤检测方法[J]. 振动与冲击, 2018, 37(7):91-95,101. JING L, YANG Z C, ZHANG J Q. Structural damage detection method based on information fusion[J]. Journal of Vibration and Shock, 2018, 37(7):91-95,101(in Chinese).
[23] FAN X, ZUO M J. Fault diagnosis of machines based on D-S evidence theory. Part 1:D-S evidence theory and its improvement[J]. Pattern Recognition Letters,2006,27(5):366-376.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References