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首页> 外文期刊>Journal of intelligent material systems and structures >Eliminating Environmental or Operational Influences in Structural Health Monitoring using the Missing Data Analysis
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Eliminating Environmental or Operational Influences in Structural Health Monitoring using the Missing Data Analysis

机译：使用缺失数据分析消除结构健康监测中的环境或运营影响

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摘要

Latent variable models can be used to eliminate the environmental or operational effects from the data without measuring the underlying variables and resulting in an increased reliability of damage detection. A method is proposed, which also utilizes the available environmental or operational variables. The method is based on the missing data analysis, in which each feature is estimated in turn using the other features and also the available environmental or operational variables. As damage detection is solely based on the measuremets, training data from the undamaged structure under different environmental or operational conditions are needed. Compared to many other latent variable models, the main advantage of the proposed method is that there are no parameters to be adjusted. The main disadvantage is a higher run time. The method is verified in a numerical study of a vehicle crane with a varying configuration and in an experimental study of a bridge structure under environmental variations. All damage cases were detected using the proposed approach, whereas no indications of damage resulted using the features directly. The importance of the measured environmental or operational variables for damage detection was found to be low, because the features typically consisted all the relevant information.
机译：潜在变量模型可用于从数据中消除环境或操作影响，而无需测量基础变量，从而提高损坏检测的可靠性。提出了一种方法，该方法还利用了可用的环境或操作变量。该方法基于丢失的数据分析，其中依次使用其他功能以及可用的环境或操作变量来估算每个功能。由于损坏检测仅基于量度，因此需要来自在不同环境或操作条件下未损坏结构的训练数据。与许多其他潜在变量模型相比，该方法的主要优点是无需调整任何参数。主要缺点是运行时间较长。该方法已在具有不同配置的车辆起重机的数值研究中以及在环境变化下的桥梁结构的实验研究中得到了验证。使用建议的方法可检测到所有损坏情况，而直接使用这些功能不会导致损坏。发现测量到的环境或操作变量对于损坏检测的重要性很低，因为这些功能通常包括所有相关信息。

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