[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124453-en":3,"doc-seo-124453-105":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":4,"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},124453,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Automated Health Monitoring in Rock Drilling via Machine Learning - Industrial mining application","The mining industry significantly impacts environmental health and worker safety, motivating advanced monitoring approaches. This dissertation applies data fusion and machine learning to improve the monitoring and analysis of rock drilling operations. A core contribution is an effective sensor placement strategy that optimizes crucial operational data collection. The work further develops a robust multi-class classification system to identify key drilling stages and reduce extreme events. Using vibration signals, the model detects unusual patterns and irregularities, and is validated by classifying five mining activities, supporting predictive maintenance and improved operational protocols.","MARZIEH ZARE  \nAutomated Health Monitoring in Rock Drilling via Machine Learning  \nIndustrial mining application  \nTampere University Dissertations 1335  \nTampere University Dissertations 1335  \nMARZIEH ZARE  \nAutomated Health Monitoring in Rock Drilling via Machine Learning Industrial mining application  \nACADEMIC DISSERTATION  \nTo be presented, with the permission of the Faculty of Information Technology and Communication Sciences of Tampere University ,  \nfor public discussion via Zoom  \non 10 October 2025, at 12 o’clock.  \nACADEMIC DISSERTATION  \nTampere University, Faculty of Information Technology and Communication Sciences Finland  \nResponsible supervisor and Custos  \nSupervisor  \nPre-examiners  \nProfessor Ari Visa Tampere University Finland  \nSenior Manager, Dr. Tomi Krogerus Kalmar Oyj  \nFinland  \nProfessor Heikki Handroos LUT University  \nFinalnd  \nAdjunct Professor Esko Juuso University of Oulu  \nFinland  \nOpponent Professor Behzad Ghodrati  \nLuleå University of Technology  \nSweden  \nThe originality of this thesis has been checked using the Turnitin Originality service.  \nCopyright ©2025 author Cover design: Roihu Inc.  \nISBN 978-952-03-4151-0 (print)  \nISBN 978-952-03-4152-7 (pdf)  \nISSN 2489-9860 (print)  \nISSN 2490-0028 (pdf)  \n[http://urn.fi/URN:ISBN:978-952-03-4152-7](http://urn.fi/URN:ISBN:978-952-03-4152-7)  \nCarbon dioxide emissions from printing Tampere University dissertations have been compensated.  \nPunaMusta Oy – Yliopistopaino Joensuu 2025  \niii  \niv  \nPREFACE  \nThis thesis was prepared as part of my doctoral studies at Tampere University and carried out in the Department of Computing and Electrical Engineering, in collaboration with Sandvik Mining and Construction. The work was conducted within the framework of the Doctoral School of Industry Innovations (DSII), whose support and funding made this project possible.  \nI am sincerely grateful to my academic supervisor, Professor Ari Visa, for his invaluable guidance, constructive feedback, and continued support throughout this research. I also acknowledge my co-supervisor, Dr. Tomi Krogerus, for his role in this work. I extend my thanks to Sirpa Launis, who supervised the project from the industry side, for her advice and for helping to bridge the collaboration between academia and industry. I extend my thanks to the technology team at Sandvik Mining and Construction for their essential support, for providing the data, and for offering the practical context that greatly enriched this research. I am further grateful to the pre-examiners for their patience in reviewing the manuscript and for their valuable comments. I would also like to thank the opponent, Professor Behzad Ghodrati, for accepting the role and for his contribution to the public defense.  \nFinally, I dedicate this work with love to my supportive parents, Sanaz and Bijan, my wonderful siblings, Alireza, Razi, and Hani, and to my beloved Hesam.  \nvi  \nABSTRACT  \nThe mining industry significantly impacts both environmental health and worker safety. This research utilizes the potential of data fusion and machine learning methodologies to advance the monitoring and analysis of rock drilling operations.  \nA key aspect of our work was to develop an effective strategy for sensor placement, optimizing the collection of crucial operational data. Our primary aim was to implement and develop a robust classification system for identifying key stages of drilling operations to prevent extreme events and reduce potential damages that could lead to substantial financial losses and safety risks.  \nBuilding on our earlier research in real-time analysis techniques and operational monitoring, we designed and developed a practical machine learning model that operates as a multi-class classifier. This model uses sensory vibration signals to detect unusual patterns and irregularities, efficiently identifying potential risks. We successfully tested and validated this model by classifying five distinct mining activities, sho","cbCaiq6wC0H5G0nK","https://ap.wps.com/l/cbCaiq6wC0H5G0nK","pdf",11097145,1,125,"English","en",105,"# Preface\n# Abstract","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It targets how to improve monitoring and analysis of rock drilling to enhance worker safety and environmental health in the mining industry.\"},{\"question\":\"How does the proposed system detect risks during drilling?\",\"answer\":\"It uses a practical machine learning model that works as a multi-class classifier and analyzes sensory vibration signals to identify unusual patterns and irregularities.\"},{\"question\":\"What is the purpose of sensor placement in this research?\",\"answer\":\"The research develops an effective sensor placement strategy to optimize the collection of crucial operational data used for reliable monitoring and classification.\"}]","Automated Health Monitoring in Rock Drilling via Machine Learning - 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