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首页> 外文会议>3rd International Workshop on Structural Health Monitoring, Sep 12-14, 2001, Stanford University >Data Normalization: A Key for Structural Health Monitoring
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Data Normalization: A Key for Structural Health Monitoring

机译：数据规范化：结构健康监测的关键

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摘要

Structural health monitoring (SHM) is the implementation of a damage detection strategy for aerospace, civil and mechanical engineering infrastructure. Typical damage experienced by this infrastructure might be the development of fatigue cracks, degradation of structural connections, or bearing wear in rotating machinery. For SHM strategies that rely on vibration response measurements, the ability to normalize the measured data with respect to varying operational and environmental conditions is essential if one is to avoid false-positive indications of damage. Examples of common normalization procedure include normalizing the response measurements by the measured inputs as is commonly done when extracting modal parameters. When environmental cycles influence the measured data, a temporal normalization scheme may be employed. This paper will summarize various strategies for performing this data normalization task. These strategies fall into two general classes: 1. Those employed when measures of the varying environmental and operational parameters are available; 2. Those employed when such measures are not available. Whenever data normalization is performed, one runs the risk that the damage sensitive features to be extracted from the data will be obscured by the data normalization procedure. This paper will summarize several normalization procedures that have been employed by the authors and issues that have arose when trying to implement them on experimental and numerical data.
机译：结构健康监测（SHM）是针对航空航天，土木和机械工程基础设施的损坏检测策略的实施。该基础设施遭受的典型损坏可能是疲劳裂纹的发展，结构连接的退化或旋转机械中的轴承磨损。对于依赖振动响应测量的SHM策略，如果要避免损坏的假阳性指示，则对于变化的操作和环境条件归一化测量数据的能力至关重要。常见归一化过程的示例包括通过提取模态参数时通常执行的通过测量的输入归一化响应测量。当环境周期影响测量数据时，可以采用时间归一化方案。本文将总结执行此数据标准化任务的各种策略。这些策略分为两大类：1.当可以使用各种环境和操作参数的度量时采用的策略； 2.没有此类措施时雇用的人员。每当执行数据标准化时，都会冒这样的风险，即数据标准化过程会掩盖要从数据中提取的损坏敏感特征。本文将总结作者已经采用的几种归一化程序，以及尝试在实验和数值数据上实施它们时出现的问题。

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