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

Steinegger, M.; Melik-Merkumians, Martin; Zajc, Johannes; Schitter, Georg

doi:10.1109/etfa.2017.8247705

Cited by 8 publications

(13 citation statements)

References 9 publications

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“…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%

Modeling languages in Industry 4.0: an extended systematic mapping study

et al. 2019

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Industry 4.0 integrates Cyber-Physical Systems with the Internet of Things to optimize the complete value-added chain. Successfully applying Industry 4.0 requires the cooperation of various stakeholders from different domains. Domain-specific modeling languages promise to facilitate their involvement through leveraging (domain-specific) models to primary development artifacts. Objective: We aim to assess the use of modeling in Industry 4.0 through the lens of modeling languages in a broad sense. Method: Based on an extensive literature review, we updated our systematic mapping study on modeling languages and modeling techniques used in Industry 4.0 [159] to include publications until February 2018. Overall, the updated study considers 3344 candidate publications that were systematically investigated until 408 relevant publications were identified. Based on these, we developed an updated map of the research landscape on modeling languages and techniques for Industry 4.0. Results: Research on modeling languages in Industry 4.0 focuses on contributing methods to solve the challenges of digital representation and integration. To this end, languages from systems engineering and knowledge representation are applied most often but rarely combined. There also is a gap between the communities researching and applying modeling languages for In

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Section: Modeling Languages Applied To Industry 40mentioning

confidence: 80%

Modeling languages in Industry 4.0: an extended systematic mapping study

et al. 2019

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“…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%

“…Ontologies have found applications in industry and in the predictive maintenance field as a tool to semantically describe data and to provide reasoning and inference processes over it. Some architectures propose using ontologies to describe data captured through sensors (Klusch et al 2015;Steinegger et al 2017), processes (Saeed et al 2019;Dibowski et al 2016) or for the delivery of results; Smoker et al 2017). Reasoning with ontologies has found applications for providing diagnostics, recognition of qualitative fault states and distinguishing between different types of failure (Delgoshaei et al 2017;Klusch et al 2015;Steinegger et al 2017;Ferrari et al 2017), and can be combined with other mechanisms for predictive maintenance purposes.…”

Section: Introductionmentioning

confidence: 99%

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A systematic review on time-constrained ontology evolution in predictive maintenance

Canito

Corchado²,

Marreiros³

2021

Artif Intell Rev

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With the modernization of industry and introduction of IoT, maintenance practices have been moving from reactive to proactive and predictive approaches. The identification of faults often relies on the analysis of real-time data provided by streams and unstructured sources. Ontologies have been applied to the maintenance field in order to add a semantic layer to the data and facilitate interoperability, and combined with other approaches for explainability and fault diagnosis, among others. In such a time-sensitive domain, it is important that ontologies go beyond static representations of the domain and allow not only for the incorporation of time related knowledge, but must also be able to adapt to new knowledge and evolve. This systematic review presents four research questions to provide a general understanding of the state of the art of the representation of time and ontology evolution in the predictive maintenance field. The results have shown that there are several ways of representing the evolution of knowledge that are fairly established and several specific evolutionary actions are discriminated and analyzed. Similarly, there is a diverse group of metrics that can be exploited to measure change and to establish evolutionary trends and even predict future stages of the ontology. Studies on the representation of time show us that it can be done either quantitative or qualitatively, with some approaches combining the two. Applications of these to the problem of ontology evolution are still in the open. Finally, results show that while applications of ontologies to the field of predictive maintenance are plenty, there are not many studies focusing on their evolution or in the effective application of their ability to reason with time constraints. The results obtained in this systematic review are particularly relevant for devising solutions that make use of the ontology's potential for time representation and evolution in the predictive maintenance field.

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“…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%