Automated Reasoning and Knowledge Inference on OPC UA Information Models

Bakakeu, Jupiter; Brossog, Matthias; Zeitler, Jochen; Franke, Jöerg; Tolksdorf, S.; Klos, Hans-Henning; Peschke, J.

doi:10.1109/icphys.2019.8780114

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…The average F1-score of M3, M4, and M5 presented values of 0.67, 0.44, and 0.42, respectively. For this configuration, M3 presented the best results, however, it is important to remark that the anomalies (14)(15)(16)(17)(18)(19) are not detected. In contrast, M4 and M5 detected the faults (14,17,18), though the average scores are lower than M3 scores.…”

Section: E Discussionmentioning

confidence: 91%

“…The MC EC M3 detected the anomalies (7,11) with F1-scores higher or equal to 0.55 and the anomalies (9,13,17) with F1-scores higher or equal to 0.34 and less than 0.42. The MC EC M4 detected the anomalies (8,14,17) with F1-scores higher or equal to 0.67 and the anomalies (7,10,11,15) with F1-scores higher or equal to 0.38 and less than 0.54. Alternatively, the EC M5 detected the anomalies (14,18) with F1-scores higher or equal to 0.68 and the anomalies (7,11,15,17,20) higher or equal to 0.31 and less than 0.54.…”

Section: B: Effects Of the Window Sizementioning

confidence: 99%

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Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Arévalo,

Alison,

Ibrahim

et al. 2024

IEEE Access

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Fault detection systems support the operator, providing insight during the decision-making while having an (unknown) fault. Data-based models are a common option for a detection system. However, systems that rely purely on data-based models are normally trained with a specific set of data, which cannot necessarily prevent data drift. Thus, an anomaly or unknown condition detection mechanism is required to handle data with new fault cases. Besides, the model's capability to adapt to the unknown condition is equally important to anomaly detection-in other words, its capability to update itself automatically. Alternatively, expert-centered models are powered by the knowledge of operators, which provides the models with production context and expert domain knowledge. The challenge lies in combining both systems and which framework can be used to achieve this fusion. We propose an adaptive information fusion methodology to define fault detection systems using evidence theory and uncertainty quantification. The main contribution of this paper is providing a general framework for the fusion of n number of information sources using the evidence theory. The fusion provides a more robust prediction and an associated uncertainty that can be used to assess the prediction likeliness. Moreover, we provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We demonstrate the information fusion approach using data from an industrial setup, which rounds up the application part of this research. Furthermore, we address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach. We validate the approach using the Benchmark Tennessee Eastman while doing an ablation study of the model update parameters.

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Section: E Discussionmentioning

confidence: 91%

Section: B: Effects Of the Window Sizementioning

confidence: 99%

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Arévalo,

Alison,

Ibrahim

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Awangga et al 157 used OWL and RDF as tools to build ontology to correlate and describe the resources contained in family planning data. Bakakeu et al 161 implemented a solution to transform the information model into an OWL ontology expressed by RDF. Alshahrani et al 162 proposed a method for generating OWL ontology from SPARQL queries using n-ary relational patterns.…”

Section: Framesmentioning

confidence: 99%

Knowledge modeling: A survey of processes and techniques

Yun

Zhang

et al. 2021

Int J Intell Syst

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“…It must be clarified in which form and where the information of the individual elements is stored and how they can be efficiently combined. Here, it is possible to combine the advantages of the OPC UA information model with the advantages of semantic web technologies [14], There have already been several approaches to implement plug & produce approaches [6], [15], [16]. Several of these systems also rely on OPC UA as communication middleware, however, OPC UA client-server communication is used.…”

Section: Related Workmentioning

confidence: 99%

Towards a Real-Time Capable Plug & Produce Environment for Adaptable Factories

Eymüller

Hanke

Hoffmann

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

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Industrial manufacturing is currently undergoing a transformation from mass production with inflexible production systems to individual production with adaptable cells. In order to ensure this adaptability of these systems, technologies such as plug & produce are needed, to integrate, modify and remove devices at runtime. Therefor an exact description of the system, the products and the capabilities / skills of the devices is essential as well as a network for communication between the devices. Deterministic data transmission is particularly important for distributed control systems. We propose an architecture for plug & produce mechanisms with hard real-time capable communication paths between the cyber-physical components using OPC UA PubSub over TSN and the ability to load and execute real-time critical tasks at runtime.

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Automated Reasoning and Knowledge Inference on OPC UA Information Models

Cited by 14 publications

References 19 publications

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Knowledge modeling: A survey of processes and techniques

Towards a Real-Time Capable Plug & Produce Environment for Adaptable Factories

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