Cognitive data imputation : case study in maintenance cost estimation

Erkoyuncu, John Ahmet (author), Namoano, Bernadin (author), Kozjek, Dominik (author), Vrabič, Rok (author)

Cost estimation is critical for effective decision making in engineering projects. However, it is often hampered by a lack of sufficient data. For this, data imputation techniques can be used to estimate missing costs based on statistical estimates or analogies with historical data. However, these techniques are often limited because they do not consider the existing knowledge of experts. In this paper, a novel cognitive data imputation technique is proposed for cost estimation that uses explanatory interactive machine learning to integrate and improve human knowledge. Through a case study in maintenance cost estimation the effectiveness of the approach is demonstrated.

• Language: English
• Keywords: artificial intelligence, maintenance, cost estimation
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Publication date: 01.01.2023
• Year: 2023
• Number of pages: Str. 385-388
• Numbering: Vol. 72, iss. 1
• PID: 20.500.12556/RUL-147995
• UDC: 004.8:658.5
• ISSN on article: 0007-8506
• DOI: 10.1016/j.cirp.2023.03.036
• COBISS.SI-ID: 158961155
• Publication date in RUL: 17.07.2023

Record is a part of a journal

• Title: CIRP annals
• Shortened title: CIRP ann.
• Publisher: Technische Rundschau, Hallwag Verlag, Colibri, Elsevier
• ISSN: 0007-8506
• COBISS.SI-ID: 170267

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: umetna inteligenca, vzdrževanje, ocena stroškov

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0270
• Name: Proizvodni sistemi, laserske tehnologije in spajanje materialov

• Funder: Other - Other funder or multiple funders
• Funding programme: Engineering & Physical Sciences Research Council
• Project number: EP/R032718/1
• Name: DigiTOP