Temperature compensation in vibration-based structural health monitoring: Static versus dynamic regression models

Vibration-based structural health monitoring systems aim to assess structural health by monitoring damage-sensitive features, such as modal parameters, over time. Deviations in modal parameters from a baseline or normal condition, representative of the healthy structure, indicate the onset of an anomaly and trigger alarms. As is known, estimated monitoring features can be influenced by several factors, including environmental and operational variabilities such as temperature fluctuations. Such variations may cause false alarms in the healthy state or can hinder the detection of anomalies when the structure is damaged. To address this issue, temperature compensation techniques are commonly employed to remove the effects of temperature fluctuations from the modal data. One prevalent approach involves developing a regression model based on a baseline database in which the independent variables are temperatures, while the target variables are the modal parameters. There are two primary categories of regression used for this purpose: static and dynamic. In the static approach, real-time temperature values are used with the corresponding samples of modal parameters. In contrast, the dynamic approach also incorporates past temperature data as independent parameters. This is particularly important due to the time delay in heat transmission from the environment to the structural core. This delay introduces a nonuniform temperature distribution within the structure, influenced by past temperature values. In this chapter, the formulations of the static and dynamic compensation are presented, and a numerical comparison between the two techniques is provided. Moreover, the experimental dataset of the KW51 bridge is used for this comparison. Different regression models and time delays are considered and discussed in terms of the probability of false alarms and the probability of damage detection.