[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128106-en":3,"doc-seo-128106-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128106,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Development of an Automated Procedure for the Damage Pattern Recognition Via Infrared Thermography and Machine Learning","Infrared thermography (IRT) enables full-field, non-destructive testing by using infrared cameras to detect and measure thermal energy emitted from a material surface. Its value lies in rapid, noninvasive identification of thermal anomalies that indicate damage-related issues, supporting preventive maintenance while improving the safety and service life of engineering structures. This work applies machine learning to IRT data collected during cyclic tests at different stress levels to track damage onset and progression on a holed woven carbon fiber reinforced plastic specimen.","Master’s thesis  \nNT NU  \nNorwegian University of Science and Technology Faculty of Engineering  \nDepartment of Mechan ica l and Industrial Engineering  \nAbel Fabregas Alvarez  \nDevelopment of an Automated Procedure for the Damage Pattern Recognition Via Infrared Thermography and Machine Learning  \nMaster’s thesis in Industrial Engineering Supervisor: Prof. Chiara Bertolin  \nCo-supervisor: Prof. Sara Gonizzi, Prof. America Califano June 2024  \nAbel Fabregas Alvarez  \nDevelopment of an Automated Procedure for the Damage Pattern Recognition Via Infrared Thermography and Machine Learning  \nMaster’s thesis in Industrial Engineering Supervisor: Prof. Chiara Bertolin  \nCo-supervisor: Prof. Sara Gonizzi, Prof. America Califano June 2024  \nNorwegian University of Science and Technology Faculty of Engineering  \nDepartment of Mechanical and Industrial Engineering  \nAbstract  \nInfrared thermography (IRT) is a full-field, non-destructive testing (NDT) method that uses infrared cameras to detect and measure thermal energy emitted from an object's surface. The utilization of IRT stems from its ability to offer a rapid, noninvasive tool for identifying thermal anomalies related to potential issues within materials and structures, facilitating preventive maintenance and ensuring the safety and longevity of engineering structures. IRT is widely used for assessing the structural integrity of materials and structures, both in laboratory environments and real-world applications, due to its ability to provide immediate visual insights into the thermal properties and anomalies of subjects under investigation.  \nThe aim of the present work is to employ different machine learning algorithms to analyze IRT data recorded during a cyclic test carried out at different stress levels, to prompt damage onset and progression on a holed woven carbon fiber reinforced plastic sample. This approach is expected to enhance the analysis of thermal data, improving the accuracy and efficiency in detecting and diagnosing tested materials or structures saving time.  \nBy integrating machine learning with IRT, the anticipated results promise not only to streamline the data analysis process but also to elevate the diagnostic capabilities beyond current limitations. Such advancements could lead to more predictive and adaptive maintenance strategies, reducing downtime and extending the lifespan of critical infrastructure and materials.  \nLooking to the future, the combination of machine learning techniques with traditional IRT analysis holds the potential for significant improvements, enhancing the predictive maintenance capabilities and overall safety of engineered (and nonengineered) structures.  \nSammendrag  \nInfrarød termografi (IRT) er en ikke-destruktiv testmetode (NDT) som brukerinfrarøde kameraer til å detektere og måle termisk energi som avgis fra overflatentil et objekt. IRT brukes fordi det er et raskt, ikke-invasivt verktøy for å identifisere termiske avvik knyttet til potensielle problemer i materialer og konstruksjoner, noesom gjør det enklere å utføre forebyggende vedlikehold og sørge for sikkerhet og lang levetid for tekniske konstruksjoner. IRT er mye brukt til å vurdere den strukturelle integriteten til materialer og konstruksjoner, både i laboratoriemiljøer ogi virkelige anvendelser, på grunn av dens evne til å gi umiddelbar visuell innsikt i determiske egenskapene og anomaliene til de undersøkte objektene.  \nMålet med dette arbeidet er å bruke ulike maskinlæringsalgoritmer til å analysere IRT-data som er registrert under en syklisk test utført ved ulike spenningsnivåer, for å avdekke skadeutvikling og -progresjon på en karbonfiberarmert plastprøve med hull. Denne tilnærmingen forventes å forbedre analysen av termiske data, og dermed øke nøyaktigheten og effektiviteten ved deteksjon og diagnostisering av testede materialer eller strukturer, noe som sparer tid.  \nVed å integrere maskinlæring med IRT vil de forventede resultatene ikke bare effektivisere dat","cbCaio6BNvT0bYhg","https://ap.wps.com/l/cbCaio6BNvT0bYhg","pdf",14725529,2,1,92,"English","en",105,"# Abstract\n## Infrared thermography and non-destructive testing\n## Machine learning analysis of cyclic test data\n## Expected impact on diagnosis and predictive maintenance\n# Sammendrag\n## Termografi og ikke-destruktiv testing\n## Mål og anvendelse av maskinlæring\n## Forventede forbedringer og fremtidsperspektiv\n# Preface","[{\"question\":\"What is the main goal of combining machine learning with infrared thermography in this thesis?\",\"answer\":\"To use machine learning algorithms to analyze infrared thermography data and improve the detection and diagnosis of damage onset and progression.\"},{\"question\":\"How is the infrared thermography data generated for the study?\",\"answer\":\"IRT data are recorded during a cyclic test carried out at different stress levels on a holed woven carbon fiber reinforced plastic sample.\"},{\"question\":\"What benefits are expected from the proposed automated approach?\",\"answer\":\"The approach is expected to streamline thermal data analysis, increase diagnostic accuracy and efficiency, and support more predictive and adaptive maintenance strategies.\"}]","Development of an Automated Procedure for the Damage Pattern Recognition Via Infrared Thermography and Machine Learning | PDF",1785944864,232,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"development-of-an-automated-procedure-for-the-damage-pattern-recognition-via-infrared-thermography-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/development-of-an-automated-procedure-for-the-damage-pattern-recognition-via-infrared-thermography-and-machine-learning/128106/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of combining machine learning with infrared thermography in this thesis?","Question",{"text":76,"@type":77},"To use machine learning algorithms to analyze infrared thermography data and improve the detection and diagnosis of damage onset and progression.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the infrared thermography data generated for the study?",{"text":81,"@type":77},"IRT data are recorded during a cyclic test carried out at different stress levels on a holed woven carbon fiber reinforced plastic sample.",{"name":83,"@type":74,"acceptedAnswer":84},"What benefits are expected from the proposed automated approach?",{"text":85,"@type":77},"The approach is expected to streamline thermal data analysis, increase diagnostic accuracy and efficiency, and support more predictive and adaptive maintenance strategies.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]