Statistical Modeling of Wavelet-Transform-Based Features in Structural Health Monitoring
In this paper, a probabilistic model is established in quantifying the uncertainty of wavelet-transform-based features in structural health monitoring (SHM), thus the decision-making in regard to damage occurrence will be conducted under a quantified confidence. Wavelet transform has been adopted in processing online-acquired data for decades, and the adaptability of wavelet transform in handling time and scale resolutions make it a powerful tool to interpret the time-variant data series. For the complexity of real SHM problems, uncertainty from both the operational/environmental variability and the inaccuracy of data acquisition hardware degrades the SHM performance. This paper aims to derive a probabilistic uncertainty quantification model to describe the distribution of wavelet-transform-based features, to facilitate more reliable SHM decision-makings, and uncertainty-induced false-positive (Type-I error) and true damage detection rate will be traded-off in a confidence-quantified sense. The distribution derived in this paper is validated via Monte Carlo simulation.
KeywordsStructural health monitoring Uncertainty quantification Statistical modeling Signal processing Wavelet transform
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