[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126181-en":3,"doc-seo-126181-105":31,"detail-sidebar-cat-0-en-105":97},{"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},126181,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Enabled Health Monitoring and Diagnosis of Engineering Systems - Doctor of Philosophy Dissertation","This dissertation presents machine learning methods for health monitoring and diagnostic analysis of engineering systems. It develops meta-learning and multi-objective optimization strategies for industrial system monitoring, including feature design, reduction, unsupervised clustering, and quality metrics, then validates performance through experimental evaluation. It also introduces pavement distress recognition using wavelet-based clustering of smartphone accelerometer data, and proposes multiple instance learning for anomaly detection on sequential real-world datasets. Results support data-driven approaches for reliable detection and clustering quality assessment.","MACHINE LEARNING ENABLED HEALTH MONITORING AND DIAGNOSIS OF ENGINEERING SYSTEMS  \nby  \nParastoo Kamranfar  \nA Dissertation  \nSubmitted to the  \nGraduate Faculty  \nof  \nGeorge Mason University  \nIn Partial fulfillment of  \nThe Requirements for the Degree  \nof  \nDoctor of Philosophy  \nComputer Science  \nCommittee:  \n  Dr. Amarda Shehu, Dissertation Director   Dr. Daniel Barbar´a, Committee Member   Dr. David Lattanzi, Committee Member   Dr. Shuochao Yao, Committee Member   Dr. David S. Rosenblum, Department Chair  \nDate:   Spring Semester 2023  \nGeorge Mason University  \nFairfax, VA  \nML-Enabled System Health Monitoring  \nA dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at George Mason University  \nBy  \nParastoo Kamranfar  \nMaster of Science  \nGeorge Mason University, 2022  \nBachelor of Science  \nAzad University South Tehran Branch, 2008  \nDirector: Dr. Amarda Shehu, Professor  \nDepartment of Computer Science  \nSpring Semester 2023  \nGeorge Mason University  \nFairfax, VA  \nCopyright © 2023 by Parastoo Kamranfar All Rights Reserved  \nDedication  \nTo my beloved parents, Mahin and Shahram, who have always been my constant source of inspiration and support throughout my academic journey. Mom and Dad, thank you for always encouraging me to pursue my dreams and for instilling in me the value of hard work and dedication. Your Unshakeable love and support have been my guiding light, and without you, I would not have been able to achieve this milestone in my life. You have always been there for me, providing guidance, comfort, and encouragement when I needed it the most. Your sacrifices and selflessness have taught me the true meaning of love and perseverance. I am grateful for all the sacrifices you have made to ensure that I receive the best education and opportunities in life. This dissertation is a testament to your unwavering support and belief in me. I hope that this achievement brings you both pride and joy, knowing that your love and support have contributed significantly to my success.  \nAcknowledgments  \nFirst and foremost, I would like to thank my advisor and mentor, Professor Shehu, for her persistent reinforcement, guidance, and encouragement throughout my research, especially during the most challenging times. Her invaluable insights and expertise have been instrumental in shaping this dissertation and have inspired me to strive for excellence. I would also like to thank Professor Lattanzi and Professor Barbara, for serving asco-advisors of my PhD dissertation. Their expertise in the field and their willingness to share their knowledge and experience helped me to develop a deeper understanding of the subject matter. I would like to express my sincere gratitude to the Office of Naval Research and the Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) for their generous support throughout my research. Their contributions have been invaluable and instrumental in the success of my work. Special thanks go to my dear husband, Hootan, whose love, support, and patience have been my constant source of strength and inspiration. His unwavering belief in me and his willingness to lend a helping hand in every aspect of my life have been indispensable throughout this journey. Thank you for being my rock and for always reminding me of the bigger picture when I felt overwhelmed. I would like to express my sincere appreciation and gratitude tomy dear sister, Maryam, for providing my parents with the love, care, and support they needed during my time studying abroad. Finally, I extend my gratitude to my friends and colleagues in the lab who have been a constant source of motivation and support. Their companionship and active involvement have made this academic journey more pleasant and stimulating.  \nTable of Contents  \nPage  \nList of Tables ........................................ vii  \nList of Figures ...............................","cbCaibkm3GCAkImT","https://ap.wps.com/l/cbCaibkm3GCAkImT","pdf",13023943,5,1,122,"English","en",105,"# List of Tables\n# List of Figures\n# Abstract\n# Introduction\n# Background\n# Meta-Learning for Industrial System Monitoring via Multi-objective Optimization\n## Summary\n## Introduction\n## Prior Work\n## Methodology\n## Experimental Evaluation\n## Conclusion\n# Pavement Distress Recognition via Wavelet-Based Clustering of Smartphone Accelerometer Data\n## Summary\n## Introduction\n## Prior Work\n## Methodology\n## Experimental Evaluation\n## Conclusion\n# Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets","[{\"question\":\"What is the main goal of this dissertation?\",\"answer\":\"To enable health monitoring and diagnostic capabilities for engineering systems using machine learning, with emphasis on meta-learning, clustering, and anomaly detection.\"},{\"question\":\"How does the dissertation approach industrial system monitoring?\",\"answer\":\"It uses meta-learning combined with multi-objective optimization, including feature design and reduction, unsupervised clustering, and metrics to evaluate cluster quality.\"},{\"question\":\"What data-driven method is used for pavement distress recognition?\",\"answer\":\"Wavelet-based featurization followed by clustering of smartphone accelerometer data, with evaluation that includes dataset analysis and the impact of noise.\"},{\"question\":\"How are anomalies detected on sequential real-world datasets?\",\"answer\":\"The dissertation applies multiple instance learning to detect anomalies over sequential datasets, leveraging the learning framework for real-world sequence patterns.\"}]","Machine Learning Enabled Health Monitoring and Diagnosis of Engineering Systems - Doctor of Philosophy Dissertation | PDF",1785903650,307,{"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":92,"head_meta":94,"extra_data":96,"updated_unix":29},"machine-learning-enabled-health-monitoring-and-diagnosis-of-engineering-systems-doctor-of-philosophy-dissertation","",{"@graph":37,"@context":91},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-enabled-health-monitoring-and-diagnosis-of-engineering-systems-doctor-of-philosophy-dissertation/126181/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83,87],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main goal of this dissertation?","Question",{"text":77,"@type":78},"To enable health monitoring and diagnostic capabilities for engineering systems using machine learning, with emphasis on meta-learning, clustering, and anomaly detection.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the dissertation approach industrial system monitoring?",{"text":82,"@type":78},"It uses meta-learning combined with multi-objective optimization, including feature design and reduction, unsupervised clustering, and metrics to evaluate cluster quality.",{"name":84,"@type":75,"acceptedAnswer":85},"What data-driven method is used for pavement distress recognition?",{"text":86,"@type":78},"Wavelet-based featurization followed by clustering of smartphone accelerometer data, with evaluation that includes dataset analysis and the impact of noise.",{"name":88,"@type":75,"acceptedAnswer":89},"How are anomalies detected on sequential real-world datasets?",{"text":90,"@type":78},"The dissertation applies multiple instance learning to detect anomalies over sequential datasets, leveraging the learning framework for real-world sequence patterns.","https://schema.org",{"og:url":53,"og:type":93,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":95,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":98},[99,103,107,111,115,120,125,128,133,136,140],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},"Comic",60,"comic",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},6,"Technology",50,"technology",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":123,"slug":124},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":126,"slug":127},30,"research-report",{"id":129,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":131,"slug":132},9,"Religion & Spirituality",20,"religion-spirituality",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":131,"slug":135},"World Cup","world-cup",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":137,"slug":139},10,"Lifestyle","lifestyle",{"id":141,"doc_module":4,"doc_module_name":47,"category_name":142,"show_sort_weight":20,"slug":143},19,"General","general"]