首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results
【2h】

Development of a Machine Learning-Based Damage Identification Method Using Multi-Point Simultaneous Acceleration Measurement Results

机译:使用多点同时加速度测量结果的机器学习损伤识别方法的开发

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

It is necessary to assess damage properly for the safe use of a structure and for the development of an appropriate maintenance strategy. Although many efforts have been made to measure the vibration of a structure to determine the degree of damage, the accuracy of evaluation is not high enough, so it is difficult to say that a damage evaluation based on vibrations in a structure has not been put to practical use. In this study, we propose a method to evaluate damage by measuring the acceleration of a structure at multiple points and interpreting the results with a Random Forest, which is a kind of supervised machine learning. The proposed method uses the maximum response acceleration, standard deviation, logarithmic decay rate, and natural frequency to improve the accuracy of damage assessment. We propose a three-step Random Forest method to evaluate various damage types based on the results of these many measurements. Then, the accuracy of the proposed method is verified based on the results of a cross-validation and a vibration test of an actual damaged specimen.
机译:有必要评估适当损坏以安全使用结构和开发适当的维护策略。虽然已经进行了许多努力来测量结构的振动以确定损坏程度,但评估的准确性不够高,因此很难说基于结构中的振动的损伤评估尚未放入实际使用。在这项研究中,我们提出了一种通过测量多个点的结构加速来评估损伤的方法,并用随机森林来解释结果,这是一种受监督的机器学习。该方法采用最大响应加速度,标准偏差,对数衰减率和自然频率来提高损伤评估的准确性。我们提出了一种三步随机森林方法,根据这些测量结果评估各种损伤类型。然后,基于交叉验证的结果和实际损坏样本的振动测试来验证所提出的方法的准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号