[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125309-en":3,"doc-seo-125309-105":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},125309,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Integration of Digital Twins and Machine Learning for Predictive Maintenance - Using APAR Method Rules in Non-residential Buildings","Non-residential buildings consume significant global energy, making HVAC efficiency and HVAC fault reliability a priority for sustainable operation. The thesis targets limitations of existing fault detection, including limited interpretability of machine learning models and inflexible rule-based approaches, by developing a hybrid predictive maintenance framework for Air Handling Units (AHUs). It combines APAR-based interpretable detection, adaptive machine-learning fault classification and prediction, and digital-twin real-time monitoring. Deployment on an AHU in Grimstad, Norway, using six months of operational data (51,000+ records) demonstrates improved fault detection across frequent and rare classes, enabling real-time visualization and maintenance planning.","Integration of Digital Twins and Machine Learning for Predictive Maintenance Using APAR Method Rules in Non-residential Buildings  \nNote: This master thesis is based on an article submitted to a peer reviewed journal.  \nHaneen Rebhi Yousef Zabadi Fitsum Asrat Zemedkun  \nMay.2025  \nMaster’s thesis in Civil Engineering  \nDepartment of Built Environment  \nFaculty of Technology, Art and Design  \nDECLARATION  \nWe, Fitsum Asrat Zemedkun and Haneen Rebhi Zabadi, certify that we are the responsible authors of this master’s thesis. It is based on an article, titled “Integration of Digital Twins and Machine Learning for Predictive Maintenance Using APAR Method Rules in Nonresidential Buildings’’ submitted to a peer-reviewed journal Energy and Buildings. During the preparation of this thesis, the authors used ChatGPT in order to improve clarity and language refinement for selected sections. It was also used for support with coding. The generated content has been reviewed, edited, and verified by the authors to ensure accuracy and relevance to the context of the thesis. This disclosure serves to acknowledge the use of ChatGPT as described above and to affirm that the authors have taken the necessary steps to validate the generated content.  \nABSTRACT  \nNon-residential buildings are the largest global energy consumers, making Heating, Ventilation, and Air Conditioning (HVAC) system efficiency a critical area of focus. Within these systems, Air Handling Units (AHUs), as central components of HVAC systems, play a key role in regulating indoor climate, but are particularly prone to operational faults due to complex control dynamics. Addressing limitations in current fault detection methods, namely the lack of interpretability in Machine Learning (ML) models and the rigidity of rule-based systems, this study aims to develop a hybrid Predictive Maintenance (PdM) framework that is both transparent and scalable. The approach combines interpretable fault detection using the Air-Handling Unit Performance Assessment Rules (APAR), adaptive fault classification and prediction through machine learning (ML), and real-time monitoring via Digital Twin (DT) interface to enhance operational reliability and energy efficiency. To evaluate feasibility, the framework was deployed on an AHU in a non-residential facility in Grimstad, Norway, using six months of operational data with over 51,000 logged records. Evaluation results show that the hybrid approach significantly improves fault detection performance across both frequent and rare classes, with strong F1-scores and high recall for critical fault conditions. The DT component, integrated via pyRevit and a web-based dashboard, enables real-time fault visualization and supports maintenance planning. The findings validate that combining expertdriven rules with ML and DT technology provides a practical, accurate, and scalable solution for PdM inAHUs. This framework supports the transition from reactive to intelligent operations in building environments.  \nKeywords: Predictive Maintenance (PdM); Air Handling Units (AHUs); Fault Detection and Diagnosis (FDD); Air-Handling Unit Performance Assessment Rules (APAR); Machine Learning (ML); Digital Twin (DT) .  \nSammendrag  \nBygninger er en stille del av livene våre. De rommer hverdager, møter, læring ogomsorg. Samtidig er de blant de største forbrukerne av energi i samfunnet. Når oppvarming, kjøling og ventilasjon ikke fungerer som de skal, påvirker det både miljøet og menneskene somoppholder seg i bygget. Men hva om bygninger kunne lære å kjenne seg selv, og si fra når noe er i ferd med å gå galt? Denne masteroppgaven handler om å gi bygninger denne evnen gjennom ny teknologi. Kjernen i arbeidet er bruk av digitale tvillinger, som enkelt forklart er en digital kopi av en fysisk bygning koblet til sanntidsdata. Ved å koble sensorer i bygget til endatamodell, kan man følge med på hva som skjer til enhver tid, for eksempel temperatur, luftstrøm eller ventilasjonsstatus.","cbCaioMgoXOFBmxE","https://ap.wps.com/l/cbCaioMgoXOFBmxE","pdf",3118712,1,69,"English","en",105,"# Abstract\n# Keywords\n# Acknowledgements","[{\"question\":\"What problem does the predictive maintenance framework address in non-residential buildings?\",\"answer\":\"It addresses HVAC-related operational faults in Air Handling Units that current fault detection methods struggle with, particularly due to low interpretability in machine learning models and rigidity in rule-based systems.\"},{\"question\":\"How does the framework combine APAR, machine learning, and digital twin technologies?\",\"answer\":\"It uses APAR for interpretable fault detection, machine learning for adaptive fault classification and prediction, and a digital twin interface for real-time monitoring and fault visualization.\"},{\"question\":\"What evidence is provided to validate the approach, and where was it tested?\",\"answer\":\"The framework was deployed on an AHU in a non-residential facility in Grimstad, Norway, using six months of operational data with 51,000+ logged records, showing improved fault detection performance for both frequent and rare fault classes.\"}]","Integration of Digital Twins and Machine Learning for Predictive Maintenance - 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