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基于应变模态差和神经网络的管道损伤识别

Damage Identification in Straight Pipeline Using Strain Modal Difference and Neural Network

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【作者】 周邵萍郝占峰韩红飞张佳程章兰珠

【Author】 Zhou Shaoping;Hao Zhanfeng;Han Hongfei;Zhang Jiacheng;Zhang Lanzhu;School of Mechanical and Power Engineering,East China University of Science and Technology;

【机构】 华东理工大学机械与动力工程学院

【摘要】 应变模态差对结构微小损伤具有很高的敏感性且对结构损伤处具有较高的定位识别率,故在工程实际中可以利用其对管道进行损伤识别。然而,应变模态差只能定性地反映结构的损伤程度,并不能直接量化损伤结构的损伤程度,故采用神经网络和应变模态差相结合的方法对损伤管道进行损伤位置和损伤程度的识别。利用有限元分析软件ANSYS进行模态分析提取管道的应变模态参数,并把管道损伤前后的应变模态差作为神经网络的输入参数,以损伤位置和损伤程度作为神经网络的输出参数,对损伤管道分别进行单损伤和双损伤的损伤定位和程度识别。研究结果表明,利用应变模态差和神经网络相结合的方法能够准确识别出管道的损伤位置以及损伤程度。

【Abstract】 Strain modal difference is used to identify the location of damage in pipelines,due to its precise recognition in engineering practice and its sensitivity to small structural damage.However,this method fails to quantify the structure′s degree of damage,so a new method that combines the neural network with the strain modal differenceis presented to identify the degree of pipeline damage.It takes the strain modal difference obtained through the finite element as the input parameter and the damage location and degreeas the output parameter of the network to identify the location and degree of both single and double damage.The simulation results of damage identification in the pipeline show that this method can not only determine the location of pipeline damage,but also accuratelyquantify the degree of damage.

【基金】 国家自然科学基金资助项目(51175178;51275171)
  • 【文献出处】 振动.测试与诊断 ,Journal of Vibration,Measurement & Diagnosis , 编辑部邮箱 ,2015年02期
  • 【分类号】TB302.5;TP18
  • 【被引频次】6
  • 【下载频次】166
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