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Bootstrapped oil quality index (OQIN) data are used to train robust machine learning models for online ageing classification from class A to class G. Correlation models predict optical fibre sensor transduced outputs including refractive index, breakdown voltage, dielectric dissipation factor, dissolved decayed products, total acid number, interfacial tension, and OQIN, enabling a shift to online/IoT prescriptive detection and supporting digital-twinning in-situ ageing assessment.","Towards intrusive non-destructive online ageing detection of transformer oil leveraging bootsrapped machine learning models  \nElele, Ugochukwu; Nekahi, Azam; Arshad, Arshad; Fofana , Issouf ; McAulay, Kate  \nPublished in:  \n2023 IEEE Electrical Insulation Conference (EIC)  \nDOI:  \n10.1109/EIC55835.2023.10177331  \nPublication date:  \n2023  \nDocument Version  \nAuthor accepted manuscript  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nElele, U, Nekahi, A, Arshad, A, Fofana , I & McAulay, K 2023, Towards intrusive non-destructive online ageing detection of transformer oil leveraging bootsrapped machine learning models. in 2023 IEEE Electrical Insulation Conference (EIC). IEEE, 2023 IEEE Electrical Insulation Conference (EIC), Quebec City, Quebec, Canada, 18/06/23 . [https://doi.org/10.1109/EIC55835.2023.10177331](https://doi.org/10.1109/EIC55835.2023.10177331)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 13. Aug. 2023  \nTowards Intrusive Non-Destructive Online Ageing Detection of Transformer Oil Leveraging Bootsrapped Machine Learning Models  \nUgochukwu Elele  School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK  \n[ugochukwu.elele@gcu.ac.uk](ugochukwu.elele@gcu.ac.uk)  \nIssoufFofana Research Chair on the Aging of Power Network Infrastructure (ViAHT) Université du Québec à Chicoutimi, QC G7H 2B1, Canada  \n[ifofana@uqac.ca](ifofana@uqac.ca)  \nAzam Nekahi   \nSchool of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK  \n[azam.nekahi@gcu.ac.uk](azam.nekahi@gcu.ac.uk)  \nKate McAulay  School of Computing, Engineering and  \nBuilt Environment, Glasgow Caledonian University, Glasgow, UK  \n[kate.mcaulay@gcu.ac.uk](kate.mcaulay@gcu.ac.uk)  \nArshad Arshad   \nSchool of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK  \n[Arshad.Arshad@gcu.ac.uk](Arshad.Arshad@gcu.ac.uk)  \nAbstract—Transformers play a crucial role in power networks, ensuring that generated electricity is delivered to consumers atthe safest voltage level, reducing losses, and enabling metering and grounding. The insulation system is critical for ensuring that the function of the power transformer is carried out safely, at the expense of its gradual deterioration over time. Most conventional oil ageing detection methods are offline and, as a result, are best suited for scheduled maintenance practices which cause risk to life, sample contamination, loss of man-hour, and risk of missing critical incipient ageing responses outside the maintenance cycle window. Oil quality index (OQIN) data sets were bootstrapped to develop robust machine learning models for online ageing classification (class A to class G). Correlation models from existing mineral oil dataset was develop to predict the refractive index (RI), breakdown voltage value (BDV), dielectric dissipation factor (DDF), dissolved decayed products (DDP), total acid number (TAN), interfacial tension (IFT), and oil quality index (OQIN) using the optical fibre sensor transduced output voltage (OFSTOV) of intensity modulated optical fibre. The existing mineral oil dataset also validates the developed OQIN machine learning model. The high correlative models presented in this paper have the potential to enable the transition from traditional offline scheduled maintenance ageing detection methods to","cbCaimkLFZToONx0","https://ap.wps.com/l/cbCaimkLFZToONx0","pdf",1124057,1,7,"English","en",105,"# Introduction\n## Role of transformers and insulation ageing\n## Limitations of conventional offline detection\n# Proposed approach\n## Bootstrapped OQIN-based machine learning models\n## Correlation models using optical fibre sensor outputs\n# Validation and potential impact\n## Predictive metrics and classifier performance\n## Online/IoT prescriptive detection and digital-twinning foundation","[{\"question\":\"Why do conventional transformer oil ageing detection methods struggle in practice?\",\"answer\":\"They are mainly offline and tied to scheduled maintenance windows, which increases risks such as life-safety hazards, sample contamination, man-hour loss, and missing incipient ageing responses occurring between maintenance cycles.\"},{\"question\":\"How are bootstrapped machine learning models used for online ageing classification?\",\"answer\":\"Oil quality index (OQIN) datasets are bootstrapped to build robust machine learning models that classify ageing into class A through class G for online use.\"},{\"question\":\"What measurements and sensor signals support the proposed prediction models?\",\"answer\":\"Correlation models use the optical fibre sensor transduced output voltage (OFSTOV) from an intensity modulated optical fibre to predict refractive index, breakdown voltage, dielectric dissipation factor, dissolved decayed products, total acid number, interfacial tension, and OQIN.\"}]","Towards Intrusive Non-Destructive Online Ageing Detection of Transformer Oil Leveraging Bootsrapped Machine Learning Models | 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do conventional transformer oil ageing detection methods struggle in practice?","Question",{"text":75,"@type":76},"They are mainly offline and tied to scheduled maintenance windows, which increases risks such as life-safety hazards, sample contamination, man-hour loss, and missing incipient ageing responses occurring between maintenance cycles.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are bootstrapped machine learning models used for online ageing classification?",{"text":80,"@type":76},"Oil quality index (OQIN) datasets are bootstrapped to build robust machine learning models that classify ageing into class A through class G for online use.",{"name":82,"@type":73,"acceptedAnswer":83},"What measurements and sensor signals support the proposed prediction models?",{"text":84,"@type":76},"Correlation models use the optical fibre sensor transduced output voltage (OFSTOV) from an intensity modulated optical fibre to predict refractive index, breakdown voltage, dielectric 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