Semantic Structuring of Elements and Capabilities in Ultra-flexible Factories

Bauernhansl, Thomas; Weyrich, Michael; Zarco, Liliana; Müller, Timo; Marks, Philipp; Schlegel, Thilo; Siegert, Jörg

doi:10.1016/j.procir.2020.04.010

Cited by 4 publications

(15 citation statements)

References 25 publications

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“…The best performance corresponds to the anomaly (8), in which the EC M3 detects the fault cases (0,1,2,6,12) often correctly, and it has limited anomaly detection. In contrast, the EC M3 presents a lower performance while applying the anomalies (7,15).…”

Section: ) Model Update Of Ecmentioning

confidence: 90%

“…The knowledge base plays a crucial role in decision assistance systems because it provides the information that supports the user when a (faulty) condition is active [12]. There are different ways to build a knowledge base, namely using ontologies [12] [14] [15], knowledge graphs [11] [16], failure mode and effects analysis (FMEA) and engineering knowledge [13] [17], and knowledge-based frameworks for multi-modal and multi-structured data [18] [19] [20] [21]. Notable examples of knowledge-based frameworks can be found in industrial applications for (prescriptive) maintenance [22] [23], machine condition assessment [13], providing work instructions [24], life cycle engineering [18], product lifecycle management [19] [25], and cyber-physical production systems [26] [27].…”

Section: A Fault Detection and Decision Assistance Systemsmentioning

confidence: 99%

“…For comparison purposes, the original label 7 is changed by the new label 30. We defined three main experiments, namely, the retraining of the ECs using all the fault cases (1,...,21), the study of the retraining parameters (e.g., threshold size, window size, and detection patience) using the fault cases (7,8,15), and the fine-tuned retrained ECs using all the faults (1,...,21). We selected the fault cases (7,8,15) as anomalies to have a case for each primary data group (easy, medium, and hard).…”

Section: ) Update Of Ecsmentioning

confidence: 99%

“…For this purpose, we test the effects of the threshold size, window size, and detection patience. We chose the MC EC M3 to perform the experiments and selected the threshold sizes (150,250,350) and anomalies (7,8,15). a: Effects of the threshold size Table 4 presents the F1-scores of the MC ECs M3, M4, and M5 trained with the fault cases (0, 1,2,6,12).…”

Section: ) Model Update Of Ecmentioning

confidence: 99%

See 3 more Smart Citations

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: ) Model Update Of Ecmentioning

confidence: 90%

Section: A Fault Detection and Decision Assistance Systemsmentioning

confidence: 99%

Section: ) Update Of Ecsmentioning

confidence: 99%

Section: ) Model Update Of Ecmentioning

confidence: 99%

See 2 more Smart Citations

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

“…It can allow shrewd generation frameworks [104][105][106][107][108]. Appropriately, the virtual plant is a coordinated, high-fidelity recreation demonstration of a fabricating plant, which offers a progressed choice bolster capability and can back the assessment and reconfiguration of unused or existing fabricating frameworks [109,110]. Therefore, real-time information integration between VRenabled VF recreations is considered to be superior with higher exactness, precision, and unwavering quality [111][112][113].…”

Section: ) Data Analytics and Real-life Data From Products Processes ...mentioning

confidence: 99%

Modeling and Simulation of Manufacturing Processes and Systems: Overview of Tools, Challenges, and Future Opportunities

Alquraish

2022

Eng. Technol. Appl. Sci. Res.

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Manufacturing is an important part of the modern economy. It is characterized by complexity in terms of systems, approaches, and interactions with intrinsic and extrinsic factors. Numerous efforts have been developed to use modeling and simulation tools to improve manufacturing efficiency and productivity and to achieve maximum quality, especially with the different mutations in the factories of today. This paper reviews the conventional and modern tools used in manufacturing system design and production improvement. Challenges that need to be addressed by the simulation community are discussed in depth. Finally, the evolution, advances, current practices, and future opportunities are discussed in the context of the contemporary manufacturing industry.

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Identification of Reconfiguration Demand and Generation of Alternative Configurations for Cyber-Physical Production Systems

Müller

Walth

Jazdi

et al. 2021

Advances in Automotive Production Technology – Theory and Application

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Semantic Structuring of Elements and Capabilities in Ultra-flexible Factories

Cited by 4 publications

References 25 publications

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Adaptive Information Fusion Using Evidence Theory and Uncertainty Quantification

Modeling and Simulation of Manufacturing Processes and Systems: Overview of Tools, Challenges, and Future Opportunities

Identification of Reconfiguration Demand and Generation of Alternative Configurations for Cyber-Physical Production Systems

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