https://www.mdu.se/

mdh.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Analysis of log files to enable smart-troubleshooting in Industry 4.0: a systematic mapping study
Mälardalens universitet, Akademin för innovation, design och teknik, Innovation och produktrealisering. Sigma Technology Information, Stockholm, Sweden.ORCID-id: 0009-0009-9081-5476
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-8027-0611
Mälardalens universitet, Akademin för innovation, design och teknik, Innovation och produktrealisering.ORCID-id: 0000-0002-2833-7196
Sigma Technology Information, Stockholm, Sweden.
(engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536Artikkel i tidsskrift (Annet vitenskapelig) Epub ahead of print
Abstract [en]

A crucial element of Industry 4.0, is the utilization of smart devices that generate log files. Log files are key components containing data on system operations, faults (unexpected glitches or malfunctions), errors (mistakes or incorrect actions), and failures (complete breakdowns or non-functionality). This paper presents a systematic mapping study analyzing research conducted on log files for smart-troubleshooting in Industry 4.0. To the best of our knowledge, this is the study that aims to identify research trends, log file attributes, techniques, and challenges involved in log file analysis for smart-troubleshooting. From an initial set of 941 potentially relevant peer-reviewed publications, 74 primary studies were selected and analyzed using a meticulous data extraction, analysis, and synthesis process. The results of the study demonstrate that the majority of research has focused on developing algorithms for log analysis, with machine learning being the most commonly used approach. The smart-troubleshooting encompasses a range of activities and tools that are essential for collecting failure data generated by diverse interconnected devices, conducting analyses, and aligning them with troubleshooting instructions and software remedies. Moreover, the study identifies the need for further research in the areas of real-time log analysis, anomaly detection, and the integration of log analysis with other Industry 4.0 technologies. In conclusion, our study provides insights into the current state of research in log analysis for smart-troubleshooting in Industry 4.0 and identifies areas for future research. The use of smart devices generating log files in Industry 4.0 highlights the importance of log file analysis for troubleshooting purposes. Further research is needed to address the challenges and opportunities in this field to integrate log analysis with other Industry 4.0 technologies for performing more efficient and effective troubleshooting.

HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-65123DOI: 10.1109/access.2023.3342365OAI: oai:DiVA.org:mdh-65123DiVA, id: diva2:1821365
Tilgjengelig fra: 2023-12-20 Laget: 2023-12-20 Sist oppdatert: 2023-12-21bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Person

Partovian, SaniaBucaioni, AlessioFlammini, Francesco

Søk i DiVA

Av forfatter/redaktør
Partovian, SaniaBucaioni, AlessioFlammini, Francesco
Av organisasjonen
I samme tidsskrift
IEEE Access

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 2 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf