MetaAnalyser - A Concept and Toolkit for Enablement of Digital Twin

Godkänd;2022;Nivå 0;2022-05-09 (sofila);Konferensartikel i tidskrift;Funder: Alstom; Tågföretagen; Norrtåg; Infranord;Transitio; Bombardier; Sweco; Omicold; Damill and partners
Digital Twin (DT) has promising impact on the life cycle management of assets in manufacturing industry. The concept of DT has become possible with digitalisation and Artificial Intelligence (AI). Data driven Machine Learning (ML) capabilities, can enhance the performance of the DT. To replicate a dynamic system, the DT should continuously receive and process incoming data in real-time. However, every time that the system receives new incoming datasets, the challenges of ML such as data preparation, feature selection, model selection and performance evaluation, slow down the development process of DT. This paper proposes a MetaAnalyser platform that automates these steps for incoming datasets in real-time. The MetaAnalyser platform through automating data preparation, feature selection, model selection and performance evaluation, is expected to increase the level of agility in the development process of DT and the efficiency of the DT during its lifecycle. The MetaAnalyser platform is demonstrated in this paper by ranking the features that affect the arrival delays in trains and ranking regression models based on their performance on the dataset.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Datorsystem hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems hsv//eng
digital twin
artificial intelligence
automated machine learning
MetaAnalyser
feature selection
model selection
Drift och underhållsteknik
Operation and Maintenance

Karim, Ramin,1964-Luleå tekniska universitet,Drift, underhåll och akustik(Swepub:ltu)raka (author)
Karim, KevinLuleå tekniska universitet,Drift, underhåll och akustik,Kungliga Tekniska högskolan (KTH), Stockholm, Sweden(Swepub:ltu)n/a (author)
Arenbro, MartinLuleå tekniska universitet,Drift, underhåll och akustik,Kungliga Tekniska högskolan (KTH), Stockholm, Sweden(Swepub:ltu)n/a (author)
Luleå tekniska universitetDrift, underhåll och akustik (creator_code:org_t)

By the author/editor: Kumari, Jaya; Karim, Ramin, 19 ...; Karim, Kevin; Arenbro, Martin

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Computer Systems

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.