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Statistical Modeling of Wavelet-Transform-Based Features in Structural Health Monitoring

  • Aral Sarrafi
  • Zhu MaoEmail author
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

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.

Keywords

Structural health monitoring Uncertainty quantification Statistical modeling Signal processing Wavelet transform 

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Copyright information

© The Society for Experimental Mechanics, Inc. 2016

Authors and Affiliations

  1. 1.Structural Dynamics and Acoustic Systems Laboratory, Department of Mechanical EngineeringUniversity of Massachusetts LowellLowellUSA

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