Guidelines for effective unsupervised guided wave compression and denoising in long-term guided wave structural health monitoring

This paper studies the effectiveness of joint compression and denoising strategies with realistic, long-term guided wave structural health monitoring data. We leverage the high correlation between nearby collections of guided waves in time to create sparse and low-rank representations. While compression and denoising schemes are not new, they are almost exclusively designed and studied with relatively simple datasets. In contrast, guided wave structural health monitoring datasets have much more complex operational and environmental conditions, such as temperature, that distort data and for which the requirements to achieve effective compression and denoising are not well understood. The paper studies how to optimize our data collection and algorithms to best utilize guided wave data for compression, denoising, and damage detection based on seven million guided wave measurements collected over 2 years.

Automated summary

This paper investigates the effectiveness of joint compression and denoising strategies using realistic, long-term guided wave structural health monitoring data. It explores how to optimize data collection and algorithms to utilize guided wave data for compression, denoising, and damage detection.