2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) 2017
DOI: 10.1109/etfa.2017.8247705
|View full text |Cite
|
Sign up to set email alerts
|

A framework for automatic knowledge-based fault detection in industrial conveyor systems

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
13
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(13 citation statements)
references
References 9 publications
0
13
0
Order By: Relevance
“…Overall, out of the 408 classified papers, 85 (20.83 %) contribute or apply DSLs to specific to Industry 4.0 challenges and total of 74 (18.14 %) papers employ UML (including variants) We also observed that leveraging UML and DSLs is not mutually exclusive in Industry 4.0 as 8 of the publications, such as [6,56,141], employ both. A total of 74 (18.14 %) papers employ knowledge representation techniques, 29 (7.11 %) papers use AutomationML [42], and 25 (6.13 %) papers use SysML to address Industry 4.0 challenges.…”
Section: Modeling Languages Applied To Industry 40mentioning
confidence: 80%
“…Overall, out of the 408 classified papers, 85 (20.83 %) contribute or apply DSLs to specific to Industry 4.0 challenges and total of 74 (18.14 %) papers employ UML (including variants) We also observed that leveraging UML and DSLs is not mutually exclusive in Industry 4.0 as 8 of the publications, such as [6,56,141], employ both. A total of 74 (18.14 %) papers employ knowledge representation techniques, 29 (7.11 %) papers use AutomationML [42], and 25 (6.13 %) papers use SysML to address Industry 4.0 challenges.…”
Section: Modeling Languages Applied To Industry 40mentioning
confidence: 80%
“…maintenance reports or manuals) (Sad-Houari et al 2019;Wang et al 2019;Cho et al 2019;Smoker et al 2017;Delgoshaei et al 2017), existing systems (e.g. Enterprise Resource Planning and Manufacturing Execution Systems) (Dibowski et al 2016;Bayar et al 2016;Cho et al 2019;Delgoshaei et al 2017), sensors (Dibowski et al 2016;Saeed et al 2019;Cho et al 2019;Klusch et al 2015;Steinegger et al 2017;Ferrari et al 2017), or combinations of these. In these cases, the representation of time is done exclusively through time points (Cho et al 2019;Delgoshaei et al 2017;Saeed et al 2019;Klusch et al 2015;Ferrari et al 2017), and no approaches explicitly using time-constrained relationships have been found.…”
Section: Rq4: What Methods Of Knowledge Acquisition and Time Representation Are Commonly Applied In The Predictive Maintenance Field?mentioning
confidence: 99%
“…In these cases, the representation of time is done exclusively through time points (Cho et al 2019;Delgoshaei et al 2017;Saeed et al 2019;Klusch et al 2015;Ferrari et al 2017), and no approaches explicitly using time-constrained relationships have been found. Finally, half of all retrieved studies propose or show potential combination of ontologies with other methods in order to achieve better results in the identification of events and potential failures, such as machine learning algorithms (Cho et al 2019;Smoker et al 2017;Steinegger et al 2017), multi-agent systems (Steinegger et al 2017), rules (Sad-Houari et al 2019;Dibowski et al 2016;Bayar et al 2016), case-based reasoning (Ansari et al 2019) and probabilistic models (Klusch et al 2015;Ferrari et al 2017).…”
Section: Rq4: What Methods Of Knowledge Acquisition and Time Representation Are Commonly Applied In The Predictive Maintenance Field?mentioning
confidence: 99%
See 1 more Smart Citation
“…A robust fault detection techniques based on consensus-based multi-agent approach for sensors networks makes use of the information interaction and coordination among the neighbouring networks for the fault detection (Jiang et al 2014). More recently, a framework for automatic generation of a flexible and modular system has been proposed for fault detection and diagnosis (Steinegger et al 2017). This methods gathers the information from various engineering artefacts using ontology and generates fault detection and diagnosis functions based on structural and procedural generation rules.…”
Section: Multi-agent Systems and Ontology For Fault Detectionmentioning
confidence: 99%