KoMMDia: Dialogue-Driven Assistance System for Fault Diagnosis and Correction in Cyber-Physical Production Systems

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

Abstract

In complex production systems, the diagnosis and correction of faults requires operators to possess a deep understanding of the specific processes and machines as well as general knowledge about the interactions between different system components. However, in actual work environments operator qualification and experience is quite diverse, which leads to an immense variability in the time required for fault diagnosis and in the quality of corrective actions. Both time and quality of fault diagnosis and correction are vital parameters in the functioning of a plant, because downtimes have severe economic consequences and thus should be kept to a minimum. With the introduction of highly complex and flexible cyber-physical production systems, these problems are aggravated as fault sources vary and diagnosis becomes even more challenging. The paper presents a concept for a self-learning assistance system that supports operators in finding and evaluating solution strategies for complex faults. This concept applies a question-answer approach which allows for an incremental, dialogue-based establishment of common ground between operators and the assistance system.

Details

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages999-1006
Number of pages8
ISBN (electronic)9781538671085
Publication statusPublished - 22 Oct 2018
Peer-reviewedYes

Publication series

SeriesIEEE International Conference on Emerging Technologies and Factory Automation, ETFA
Volume2018-September
ISSN1946-0740

Conference

Title23rd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2018
Duration4 - 7 September 2018
CityTorino
CountryItaly

External IDs

ORCID /0000-0001-5165-4459/work/174432567