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基于Lamb波损伤特征融合模型的金属结构件疲劳裂纹扩展预测

Fatigue crack extension prediction of metal structure component based on Lamb wave damage feature fusion model

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【作者】 王晓龙金韩微张博文杨秀彬何玉灵慈铁军

【Author】 WANG Xiaolong;JIN Hanwei;ZHANG Bowen;YANG Xiubin;HE Yuling;CI Tiejun;Department of Mechanical Engineering, North China Electric Power University;Hebei Key Laboratory of Electric Machinery Health Maintenance and Failure Prevention,North China Electric Power University;

【通讯作者】 何玉灵;

【机构】 华北电力大学机械工程系华北电力大学河北省电力机械装备健康维护与失效预防重点实验室

【摘要】 针对动态载荷环境下机械装备金属结构件疲劳裂纹扩展延伸问题,提出一种基于Lamb波损伤特征融合的疲劳裂纹扩展预测模型。首先从Lamb波信号传播特性出发,分析对比对称模式下Lamb波损伤信号和基准信号传播过程中的特性变化,在此基础上提取能够敏感表达结构损伤的相关系数和相位差异特征,通过变量标准化变换后进一步构建出疲劳裂纹扩展预测模型,最后利用金属结构件全寿命周期疲劳实验数据对模型进行验证。结果表明:所提模型能够准确预测动态载荷环境下疲劳裂纹扩展过程,与其他预测模型相比优势明显,可为机械装备关键结构件合理检修计划制定提供一定参考借鉴。

【Abstract】 In order to solve the fatigue crack extension problem of metal structure component of mechanical equipment under dynamic load environment, a fatigue crack extension prediction model based on Lamb wave damage feature fusion is proposed. Firstly, starting from the spread characteristics of Lamb wave signal, the characteristic variations of Lamb wave damage signal and benchmark signal during spread process under symmetry mode are analyzed and compared, the correlation coefficient and the phase difference features, which can sensitively express the structure damage, are extracted on this basis, and the fatigue crack extension prediction model is further constructed after variable normalization transformation. Finally, this model is verified using the whole life cycle fatigue experimental data of metal structure component. The results show that the proposed model can accurately predict the fatigue crack extension process under dynamic load environment, the proposed model has obvious advantages compared with other prediction models, and can provide a certain reference for making reasonable maintenance plan of mechanical equipment key structure component.

【基金】 国家自然科学基金资助项目(52005180);河北省自然科学基金资助项目(E2022502003);河北省高等学校科学技术研究项目(QN2022190);中央高校基本科研业务费专项资金资助项目(2023MS127)
  • 【文献出处】 中国工程机械学报 ,Chinese Journal of Construction Machinery , 编辑部邮箱 ,2024年02期
  • 【分类号】TG111.8
  • 【下载频次】43
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