Statistical Partial Wavefield Imaging (SPWI) is a powerful signal processing algorithm designed to detect and localize structural damage using sparse ultrasonic sensor arrays. Unlike traditional imaging methods that require dense sensor grids or full wavefield measurements, SPWI extracts high-quality damage localization images from partial and limited measurements, making it ideal for real-world Structural Health Monitoring (SHM) applications.
Guided wave Structural Health Monitoring (SHM) systems are powerful tools for detecting damage in structures like aircraft fuselages, pipelines, and bridges. However, one major obstacle persists: temperature variation. Even small temperature changes can significantly distort guided wave signals, often leading to false positives or missed damage.
Enter Dynamic Time Warping (DTW) Temperature Compensation — a robust, data-driven algorithm developed by Alexander Douglass and Dr. Joel B. Harley that aligns guided wave signals distorted by temperature, improving the accuracy and reliability of damage detection without requiring physical models or manual calibration.
K-SVD Dictionary Learning for Damage Detection is a baseline-free, data-driven approach for detecting structural damage using guided ultrasonic waves. Developed by Supreet Alguri and Dr. Joel B. Harley, this method eliminates the need for pristine baseline measurements—a major limitation in many real-world Structural Health Monitoring (SHM) systems.
This CodeOcean capsule provides a reproducible implementation of the algorithm, as described in the paper:
Temporal Sparse Wavenumber Analysis (TSWA) is a novel technique that reconstructs high-resolution spatiotemporal wavefields using fewer temporal samples than traditional methods typically require. Developed by Soroosh Sabeti and Dr. Joel B. Harley, TSWA makes it possible to retrieve accurate guided wave information from temporally undersampled data, which is crucial in applications where high-speed sensing, data storage, or power consumption are limiting factors.
Data-Driven Matched Field Processing (DDMFP) is an innovative signal processing framework designed for localizing acoustic sources in complex environments, such as those encountered in structural health monitoring (SHM) using Lamb waves. Traditional matched field processing (MFP) techniques rely heavily on accurate physical models of the propagation medium, which can be challenging to obtain in real-world scenarios. DDMFP circumvents this limitation by constructing localization models directly from measured data, enhancing robustness and accuracy in complex, multimodal propagation environments.
Sparse Wavenumber Analysis (SWA) is a signal processing algorithm designed to extract high-resolution wavenumber information from spatial wavefield measurements—especially when that data is sparse, noisy, or irregularly sampled. Developed by Dr. Joel B. Harley and collaborators, SWA enables researchers and engineers to analyze wave propagation with unprecedented clarity, even in situations where traditional Fourier-based methods fall short.
This CodeOcean capsule contains a complete, reproducible implementation of the SWA algorithm, based on the foundational work in:
This CodeOcean capsule contains a complete, reproducible implementation of the SWA algorithm, based on the foundational work in:
Guided waves like Lamb modes are reshaping how we inspect, model, and understand solid materials — all by listening to vibrations within the structure itself.
If you’ve never heard of Lamb waves, you’re not alone. Though they’ve been known to physicists and engineers for over a century, they remain surprisingly underdiscussed outside specialized fields like non-destructive testing, ultrasonics, and solid mechanics. But behind the scenes, Lamb waves are playing a crucial role — helping us understand how materials behave, age, and break, all through the language of wave motion.