Crack damage detection of bridge crane girder based on acoustic emission signals
-
摘要: 为了对桥式起重机的损伤情况进行判断,提出了一种基于声发射信号强度与持续时间关系的裂纹损伤检测方法。该方法通过对不同工况下声发射信号的持续时间和信号强度的关联点图的对比分析,提出了一种用于检测裂纹损伤的斜率趋势指标,并对桥式起重机主梁完整结构和与其材料相同的预制裂纹试验件进行了试验。结果表明,无缺陷起重机结构与缺陷起重机结构获得的关联图具有不同的斜率趋势,可以为起重机损伤情况的判断提供参考。Abstract: In order to judge the damage condition of bridge crane, a crack damage detection method of bridge crane was proposed based on the relationship between acoustic emission signal intensity and duration. By comparing and analyzing the correlation point plots of acoustic emission signal duration and signal intensity under different working conditions, a slope trend index for crack damage detection was proposed. An experimental study was carried out on the whole structure of bridge crane girder and the prefabricated crack test pieces of the same material. The experimental results showed that the correlation figures of the faultless crane structure and the defective structure had different slope trends. It provided a method and reference for judging crane damages by using acoustic emission technology.
-
Keywords:
- bridge crane /
- crack defect /
- acoustic emission /
- characteristic parameter
-
-
[1] 王岩, 丁克勤, 赵娜. 大型港机金属结构损伤模式探究[J]. 科技信息, 2013(3):13, 17. [2] 市场监管总局. 关于2020年全国特种设备安全状况的通告[J]. 西部特种设备, 2021, 4(2):5-8. [3] 李孟源, 尚振东, 蔡海潮. 声发射检测及信号处理[M]. 北京:科学出版社, 2010:1-24. [4] 杨浩宇, 骆红云, 陈国伟, 等. 桥式起重机Q345B钢箱形梁母材疲劳损伤的声发射双谱分析[J]. 起重运输机械, 2018(1):67-72. [5] 孙岱华, 王宝龙, 郝宏伟. 履带起重机桁架式臂架破坏性试验过程的声发射特性研究[J]. 中国特种设备安全, 2021, 37(4):69-74. [6] 杨强, 付帅, 秦勇, 等. 塔机材料常规缺陷的声发射定位与辨识技术[J]. 机械设计与制造, 2018(增1):154-156. [7] 沈功田. 声发射检测技术及应用[M]. 北京:科学出版社, 2015:50-53. [8] 孙振国, 李明宝, 陈冲, 等. 水泥土力学性能与声发射参数持续时间的关系研究[J]. 科技创新与应用, 2017(28):195-196. [9] XU JIANGONG. Nondestructive evaluation of prestressed concrete structures by means of acoustic emissions monitoring[D]. Auburn:Auburn University, 2008.
[10] MD NOR N, BUNNORI N M, IBRAHIM A, et al.Relationship between acoustic emission signal strength and damage evaluation of reinforced concrete structure:case studies[C]//2011 IEEE Symposium on Industrial Electronics and Applications.Langkawi, Malaysia:IEEE, 2011:308-313.
[11] 胡天真. 基于主成分分析和深度信念网络的轴承智能故障诊断[D].南京:南京航空航天大学, 2020.
计量
- 文章访问数: 7
- HTML全文浏览量: 0
- PDF下载量: 5