Representative Dataset Generation Framework for AI-based Failure Analysis during real-time Validation of Automotive Software Systems

Simple item page
dc.contributor.authorAbboush, Mohammad
dc.contributor.authorKnieke, Christoph
dc.contributor.authorRausch , Andreas
dc.date.accessioned2023-12-26T18:53:26Z
dc.date.available2023-12-26T18:53:26Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.877
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other99c37eaf-0041-4953-b92e-fc9ebb0dcd48
dc.identifier.urihttps://hdl.handle.net/10125/107263
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAI-based Methods and Applications for Software Engineering
dc.subjectautomotive systems development
dc.subjectfailure analysis
dc.subjectfault injection
dc.subjecthil testing
dc.subjectmachine learning.
dc.titleRepresentative Dataset Generation Framework for AI-based Failure Analysis during real-time Validation of Automotive Software Systems
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractRecently, thanks to its ability of extracting knowledge from historical data, the data-driven approach has been widely used in various phases of the system development life cycle. In real-time system validation, remarkable achievements have been accomplished in developing an intelligent failure analysis based on historical data. However, despite its superiority over other conventional approaches, e.g., model-based and signal-based, the availability of representative datasets persists as a major challenge. Thus, for different engineering applications, new solutions to generate representative faulty data in different forms should be explored. Therefore, in this study, a novel approach based on Hardware-in-the-Loop (HIL) simulation and real-time Fault Injection (FI) method is proposed to generate and collect data samples under single and simultaneous faults for Machine Learning (ML) applications during system validation phases. The developed framework can generate not only sequential data, but also textual data including fault logs. The results show the applicability of the proposed framework in simulating and capturing the system behaviour under faults within the system components.
dcterms.extent10 pages
prism.startingpage7312

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0714.pdf
Size:
8.35 MB
Format:
Adobe Portable Document Format
Download

Collections

AI-based Methods and Applications for Software Engineering