{"id":1256,"date":"2025-06-24T13:13:18","date_gmt":"2025-06-24T13:13:18","guid":{"rendered":"https:\/\/smartdata.ece.ufl.edu\/?p=1256"},"modified":"2026-04-07T13:09:37","modified_gmt":"2026-04-07T13:09:37","slug":"statistical-partial-wavefield-imaging-repository","status":"publish","type":"post","link":"https:\/\/smartdata.ece.ufl.edu\/index.php\/2025\/06\/24\/statistical-partial-wavefield-imaging-repository\/","title":{"rendered":"Statistical Partial Wavefield Imaging Repository"},"content":{"rendered":"\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-50\"><a class=\"wp-block-button__link wp-element-button\">Get the Code (Code Ocean)<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Summary<\/h2>\n\n\n\n<p><strong>Statistical Partial Wavefield Imaging (SPWI)<\/strong> is a powerful signal processing algorithm designed to detect and localize structural damage using <strong>sparse ultrasonic sensor arrays<\/strong>. Unlike traditional imaging methods that require dense sensor grids or full wavefield measurements, SPWI extracts high-quality damage localization images from <strong>partial<\/strong> and <strong>limited measurements<\/strong>, making it ideal for real-world Structural Health Monitoring (SHM) applications.<\/p>\n\n\n\n<p>This <a class=\"\" href=\"https:\/\/codeocean.com\/capsule\/3444631\/tree\/v1\">CodeOcean capsule<\/a> contains a complete implementation of the SPWI algorithm as introduced in:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p><strong>Harley, J. B., &amp; Chia, C. C. (2017).<\/strong> <em>Statistical partial wavefield imaging using Lamb wave signals<\/em>. Structural Health Monitoring, 17(4), 932\u2013944.<\/p>\n<\/blockquote>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">\ud83e\udde9 The Problem: Sparse Data, Incomplete Fields<\/h3>\n\n\n\n<p>Lamb waves are widely used in SHM due to their ability to travel long distances and interact sensitively with damage. But generating high-resolution images of damage typically requires:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Dense wavefield measurements,<\/li>\n\n\n\n<li>Full baseline datasets, and<\/li>\n\n\n\n<li>Detailed physical modeling.<\/li>\n<\/ul>\n\n\n\n<p>In many practical cases (e.g., aircraft, bridges, pipelines), this level of sensing is <strong>impractical or impossible<\/strong>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">\ud83e\udde0 The Solution: Statistical Inference from Partial Fields<\/h3>\n\n\n\n<p>SPWI solves this by using <strong>partial wavefield data<\/strong>\u2014a small subset of wave measurements\u2014combined with a <strong>statistical model<\/strong> that estimates how signals should behave across the structure. It then builds an image that statistically highlights <strong>anomalous behavior<\/strong>\u2014which may correspond to <strong>damage<\/strong>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Core Idea:<\/h4>\n\n\n\n<p>Let <img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/smartdata.ece.ufl.edu\/wp-content\/ql-cache\/quicklatex.com-0ab7ba7a9e086ff8eab52fe1e384ad13_l3.png\" class=\"ql-img-inline-formula quicklatex-auto-format\" alt=\"&#117;&#40;&#120;&#44;&#32;&#116;&#41;\" title=\"Rendered by QuickLaTeX.com\" height=\"19\" width=\"47\" style=\"vertical-align: -5px;\"\/> be a measured wavefield at position xxx and time ttt, and suppose we have access to only a <strong>limited number of these measurements<\/strong>.<\/p>\n\n\n\n<p>The algorithm computes <strong>a statistical similarity metric<\/strong> between each region\u2019s measured signal and an expected \u201chealthy\u201d response using: <p class=\"ql-center-displayed-equation\" style=\"line-height: 37px;\"><span class=\"ql-right-eqno\"> &nbsp; <\/span><span class=\"ql-left-eqno\"> &nbsp; <\/span><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/smartdata.ece.ufl.edu\/wp-content\/ql-cache\/quicklatex.com-51104faa8332ff66ca20269826cee7dd_l3.png\" height=\"37\" width=\"335\" class=\"ql-img-displayed-equation quicklatex-auto-format\" alt=\"&#92;&#91;&#32;&#83;&#40;&#120;&#41;&#32;&#61;&#32;&#92;&#115;&#117;&#109;&#95;&#123;&#116;&#125;&#32;&#92;&#108;&#101;&#102;&#116;&#124;&#32;&#117;&#95;&#123;&#92;&#116;&#101;&#120;&#116;&#123;&#109;&#101;&#97;&#115;&#117;&#114;&#101;&#100;&#125;&#125;&#40;&#120;&#44;&#32;&#116;&#41;&#32;&#45;&#32;&#117;&#95;&#123;&#92;&#116;&#101;&#120;&#116;&#123;&#101;&#120;&#112;&#101;&#99;&#116;&#101;&#100;&#125;&#125;&#40;&#120;&#44;&#32;&#116;&#41;&#32;&#92;&#114;&#105;&#103;&#104;&#116;&#124;&#94;&#50;&#32;&#92;&#93;\" title=\"Rendered by QuickLaTeX.com\"\/><\/p><\/p>\n\n\n\n<p>Then, it projects this error across a spatial grid, forming a <strong>statistical image<\/strong> of potential damage regions.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">\ud83e\uddea What Makes SPWI Unique?<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\ud83d\udcc9 <strong>Works with sparse data<\/strong>: Effective even with fewer than 10% of full-field measurements.<\/li>\n\n\n\n<li>\ud83e\udde0 <strong>Model-free<\/strong>: Doesn\u2019t require a physics-based model of wave propagation.<\/li>\n\n\n\n<li>\ud83d\udd0d <strong>Highlights anomalies<\/strong> statistically\u2014rather than just visually or heuristically.<\/li>\n\n\n\n<li>\ud83d\udd04 <strong>Compatible with many wave types<\/strong>: Primarily Lamb waves, but generalizable.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">\ud83d\udce6 What\u2019s Inside This Capsule?<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u2705 MATLAB code implementing the full SPWI algorithm<\/li>\n\n\n\n<li>\ud83d\udcc2 Sample Lamb wave data (experimental or simulated)<\/li>\n\n\n\n<li>\ud83d\udcca Image generation scripts for damage visualization<\/li>\n\n\n\n<li>\ud83d\udcd8 Documentation to guide users through input\/output<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">\ud83d\udee0\ufe0f Why Use SPWI?<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\ud83d\ude80 <strong>Practical<\/strong>: Works with real-world limitations on sensors and data<\/li>\n\n\n\n<li>\ud83e\udde0 <strong>Statistically robust<\/strong>: Based on quantitative comparison, not qualitative interpretation<\/li>\n\n\n\n<li>\ud83e\uddf1 <strong>Modular<\/strong>: Can be integrated with existing SHM systems or extended with machine learning<\/li>\n\n\n\n<li>\ud83c\udf0d <strong>Field-proven<\/strong>: Validated in both lab and realistic environments<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">\ud83d\udcda Reference<\/h3>\n\n\n\n<p><strong>Harley, J. B., &amp; Chia, C. C. (2017).<\/strong> <em>Statistical partial wavefield imaging using Lamb wave signals<\/em>. <em>Structural Health Monitoring<\/em>, 17(4), 932\u2013944.<br>\ud83d\udd17 <a>https:\/\/doi.org\/10.1177\/1475921717735329<\/a><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>This capsule provides a ready-to-use toolkit for turning <strong>limited Lamb wave data<\/strong> into <strong>powerful damage images<\/strong>. Whether you&#8217;re working with embedded sensors in composite wings or field inspections on civil infrastructure, SPWI helps you <strong>see more with less<\/strong>.<\/p>\n\n\n\n<p>Let data scarcity be an opportunity\u2014not a limitation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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.<\/p>\n","protected":false},"author":1,"featured_media":925,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[16,14,35,40,13,39],"class_list":["post-1256","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-code-resources","tag-linear-algebra","tag-nondestructive-testing","tag-physical-mismatch","tag-sparse-wavenumber-analysis","tag-structural-health-monitoring","tag-undersampling"],"_links":{"self":[{"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/posts\/1256","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/comments?post=1256"}],"version-history":[{"count":1,"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/posts\/1256\/revisions"}],"predecessor-version":[{"id":1257,"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/posts\/1256\/revisions\/1257"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/media\/925"}],"wp:attachment":[{"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/media?parent=1256"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/categories?post=1256"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/smartdata.ece.ufl.edu\/index.php\/wp-json\/wp\/v2\/tags?post=1256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}