Abstract
AbstractThis article presents probabilistic methodologies toward the design of a reliable structural health monitoring system, by addressing several types of uncertainties in sensor performance and data, including sensor layout effects and sensor data noise, incompleteness, and variability. The sensor layout is designed to maximize the reliability of damage detection by combining probabilistic finite element analysis, damage detection algorithms, and reliability‐based optimization techniques. A Bayesian discrete wavelet packet transform‐based denoising approach is presented to perform data cleansing prior to damage detection. A nonparametric system identification method is applied when using incomplete sensor data. A Bayesian method is presented to incorporate possible uncertainties in both sensor data and model prediction and to provide a quantitative measure of confidence in structural condition assessment. These methodologies are illustrated for application to an aerospace structure thermal protection system panel and to a five‐story building frame structure, representing two different disciplines.
Published Version
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