Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models

TL;DR

This paper introduces a probabilistic damage detection method using stochastic time series models and SVD/PCA modifications, enabling automatic and effective structural health monitoring with guided ultrasound in various materials.

Contribution

It presents a novel probabilistic damage detection scheme that simplifies the process through model parameter modifications and statistical thresholds, enhancing automation and accuracy in SHM.

Findings

High detection accuracy in metallic and composite coupons

Effective damage identification across multiple scenarios

Potential for automated structural health monitoring

Abstract

In this work, a probabilistic damage detection and identification scheme using stochastic time series models in the context of acousto-ultrasound guided wave-based SHM is proposed, and its performance is assessed experimentally. In order to simplify the damage detection and identification process, model parameters are modified based on the singular value decomposition (SVD) as well as the principal component analysis (PCA)-based truncation approach. The modified model parameters are then used to estimate a statistical characteristic quantity that follows a chi-squared distribution. A probabilistic threshold is used instead of a user-defined margin to facilitate automatic damage detection. The method's effectiveness is assessed via multiple experiments using both metallic and composite coupons and under various damage scenarios using damage intersecting and damage non-intersecting paths. The results of the study confirm the high potential and effectiveness of the stochastic time series methods for guided wave-based damage detection and identification in a potentially automated way.