基于多传感器的裂纹扩展监测研究

常琦; 杨维希; 赵恒; 孟瑶; 刘君; 高鹤明

doi:10.7527/S1000-6893.2019.23336

航空学报 >

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

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

固体力学与飞行器总体设计

基于多传感器的裂纹扩展监测研究

常琦 ,
杨维希 ,
赵恒 ,
孟瑶 ,
刘君 ,
高鹤明

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西安理工大学机械与精密仪器工程学院, 西安 710048

收稿日期: 2019-08-02

修回日期: 2019-09-09

网络出版日期: 2020-03-03

基金资助

陕西省自然科学基金面上项目（2018JM5112）；陕西省教育厅自然科学专项（15JK1496）；国家自然科学基金青年基金（51406164）；国家自然科学基金（51775429）

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A multi-sensor based crack propagation monitoring research

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

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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)

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摘要

疲劳裂纹扩展是结构健康监测的主要问题之一，为了保证金属结构的可靠和安全运行，实时监测结构的疲劳裂纹扩展过程十分必要。针对结构裂纹扩展的问题，采用压电传感器（PZT）和电阻应变片两种传感器，提出结合能够连续监测结构损伤的被动监测方法以及对微小损伤敏感的主动监测方法对裂纹扩展进行综合监测，以提高裂纹扩展的监测水平。采用随机森林算法对裂纹长度进行识别，并使用D-S证据理论对两种传感器的识别结果进行数据融合，得到比单一传感器更准确、可靠的裂纹扩展识别结果。进行了基于应变和主动Lamb波的裂纹扩展监测实验研究，验证了该方法对提高裂纹扩展监测识别准确率的有效性和实用性。

关键词： 疲劳裂纹扩展; Lamb波; 随机森林(RF); 数据融合; D-S证据理论

本文引用格式

常琦 , 杨维希 , 赵恒 , 孟瑶 , 刘君 , 高鹤明 . 基于多传感器的裂纹扩展监测研究[J]. 航空学报, 2020 , 41(2) : 223336 -223336 . DOI: 10.7527/S1000-6893.2019.23336

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

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