[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119814-en":3,"doc-seo-119814-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119814,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine learning based anomaly detection for industry 4.0 systems - Doctoral dissertation","This doctoral work investigates machine learning approaches for anomaly detection in Industry 4.0 systems, aiming to improve reliability in industrial environments where irregular behaviors may indicate faults, risks, or process deviations. The study organizes background, motivation, and research questions around supervised and semi-supervised ensembling strategies, and develops concepts spanning ensemble learning, multiple anomaly-detection families, and domain-specific time and frequency patterns. Case studies cover smart water treatment, industrial quality testbeds, and embedded IoT settings.","Machine learning based anomaly detection for industry 4.0  \nsystems  \nDavid Velásquez Rendón  \nUniversidad del País Vasco/Euskal Herriko Unibertsitatea  \nJanuary 2023  \n(cc)2023 DAVID VELASQUEZ RENDON (cc by 4.0)  \nii  \nUNIVERSITY OF THE BASQUE COUNTRY  \nFaculty of Informatics  \nMachine learning based anomaly detection for industry 4.0 systems  \nSupervised by:  \nBasilio Sierra Araujo  \nand Mikel Maiza Galparsoro  \nSubmitted by:  \nDavid Velásquez Rendón  \nFor the academic degree of Doctoral Programme in Informatics  \nEngineering  \nJanuary 2023  \niv  \nAcknowledgements  \nThis thesis is dedicated to my father, who has supported me throughout all my studies, enabling me to achieve success in my life. I am deeply grateful for your reliable presence, your unwavering support, and for being a source of stability, joy, and strength in our family. You are my role model, and I will always carry you in my heart. My mother, who has also supported me with her unconditional love and encouragement, also deserves a special mention. Thank you for always being therefor me. I would also like to extend my gratitude to my uncle Juan Carlos, who has played a significant role in my life, being like a second dad and always being there to lend a helping hand.  \nI am also thankful to EAFIT University, the University of the Basque Country, and the Vicomtech Foundation for providing me with the opportunity to undertake this thesis through their sponsorship and research environment. I express my appreciation to Ricardo Mejia for introducing me to Vicomtech and to Jorge Posada for giving me this opportunity. Additionally, I would like to thank Ricardo Taborda for his support and assistance from EAFIT University.  \nI am incredibly grateful to my tutors, Mikel Maiza and Professor Basilio Sierra, who provided me with the guidance, motivation and encouragement necessary to achieve my PhD goals. Their expertise in the field has been invaluable to me and I am so thankful for their sharing their knowledge with me. My appreciation also goes to Professor Mauricio Toro for his guidance from EAFIT University and for his contributions to the scientific aspects of my thesis, his support was fundamental in the completion of my research project. Their unwavering support, time and knowledge has made this project a success and I am deeply grateful for it.  \nI would like to extend my sincere appreciation to my friends, who have been there to support me during all the challenging moments of this process. Their encouragement and support have been invaluable in helping me to grow personally. In particular, I would like to thank Julian, Tony, Dider, Angelica, Laura, Camilo, and David.  \nI am also grateful to my colleagues at Vicomtech, especially those in the Data Intelligence for Energy and Industrial Processes department, who have provided me  \nwith opportunities to share my experiences and enjoy coffee breaks together. I would like to extend a special mention to Juan Odriozola, who has not only been a friend but has also introduced me to the delights of Basque gastronomy. I would also like to thank Mikel Lopez for his help with work-related challenges and for his constant companionship.  \nFinally, I want to extend my gratitude to all my family for their support and for the wonderful times we have shared throughout this journey.  \nDavid Velásquez Rendón January 2023 Donostia-San Sebastián  \nContents  \nAcknowledgements vi  \nList of Figures xii  \nList of Tables xiii  \nGlossary xv  \nAbstract xix  \nI Body of the dissertation 1  \n1 Background 3  \n1.1 General introduction ........................... 3  \n1.2 Research environment .......................... 4  \n1.2.1 University EAFIT ......................... 5  \n1.2.2 Vicomtech ............................. 5  \n1.2.3 Projects .............................. 7  \n1.3 Structure of the dissertation ....................... 9  \n2 Motivation 11  \n2.1 Boundaries ................................ 11  \n2.2 Research questions ......................","cbCaimUzpLD7KBMh","https://ap.wps.com/l/cbCaimUzpLD7KBMh","pdf",25680685,1,223,"English","en",105,"# Acknowledgements\n# List of Figures\n# List of Tables\n# Glossary\n# Abstract\n# Body of the dissertation\n## Background\n## Motivation\n## Theoretical concepts\n## Architecture\n## Data Acquisition\n## Supervised Ensembling\n## Semi-supervised Ensembling","[{\"question\":\"What is the main focus of this doctoral research?\",\"answer\":\"The research focuses on machine learning-based anomaly detection for Industry 4.0 systems, emphasizing improved detection in industrial and IoT contexts.\"},{\"question\":\"Which ensembling approaches are explored?\",\"answer\":\"The dissertation develops supervised ensembling and semi-supervised ensembling methods for anomaly detection across the presented case studies.\"},{\"question\":\"What types of case studies are included?\",\"answer\":\"The work includes smart water wastewater treatment, industrial quality testbench systems with rotary pneumatic machines, and a smart IoT embedded system scenario.\"}]","Machine learning based anomaly detection for industry 4.0 systems - 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