| Title: | A ǂFramework for applying data-driven AI/ML models in reliability |
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| Authors: | ID Hribar, Rok, Institut Jožef Stefan (Author)
ID Antoniou, Margarita, Institut Jožef Stefan (Author)
ID Papa, Gregor, Institut Jožef Stefan (Author) |
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| Files: |  URL - Source URL, visit https://link.springer.com/chapter/10.1007/978-3-031-59361-1_12
 PDF - Presentation file. (497,12 KB, This file will be accessible after 22.04.2026)
MD5: EDA00360935E968892AF14DE5913FB82
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| Language: | English |
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| Typology: | 1.16 - Independent Scientific Component Part or a Chapter in a Monograph |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | In this chapter, we present a framework for applying artificial intelligence (AI)/machine learning (ML) in reliability, in the context of the iRel40 project. Data-driven models are becoming an increasingly fruitful tool for detecting patterns in complex data and identifying the circumstances in which they occur. Using only data, gathered along the value chain, data-driven methods are now being used to detect indications of potential early failures, signs of wear out or degradation, and other unwanted events within the development, fabrication, or service phases of the electronic components and systems. We present general considerations that were found to be important during the iRel40 project, when designing pipelines that combine data processing with the AI/ML models for predicting or detecting reliability issues. This chapter serves as an introduction to the definitions and concepts used within the specific use cases that rely on the AI/ML methodology within the iRel40 project. |
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| Keywords: | machine learning, artificial intelligence, data-driven models |
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| Publication status: | Published |
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| Publication version: | Author Accepted Manuscript |
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| Publication date: | 22.04.2024 |
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| Publisher: | Springer |
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| Year of publishing: | 2024 |
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| Number of pages: | 1 spletni vir (1 PDF dokument (323–337 str.)) |
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| Source: | Švica |
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| PID: | 20.500.12556/DiRROS-19675  |
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| UDC: | 004.8 |
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| DOI: | 10.1007/978-3-031-59361-1_12 |
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| COBISS.SI-ID: | 202399235 |
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| Copyright: | © 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 22. 7. 2024;
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| Publication date in DiRROS: | 23.07.2024 |
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| Views: | 230 |
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| Downloads: | 94 |
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