Semantic Integration of Bosch Manufacturing Data Using Virtual Knowledge Graphs

Kalaycı, Elem Güzel; González, Irlan Grangel; Lösch, Felix; Xiao, Guohui; ul-Mehdi, Anees; Kharlamov, Evgeny; Calvanese, Diego

doi:10.1007/978-3-030-62466-8_29

Cited by 38 publications

(21 citation statements)

References 16 publications

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“…-Modelling of domain knowledge in ontologies (Svetashova et al 2020a, b;Zhou et al 2020bZhou et al , 2021a, knowledge graphs (Zhou et al 2021b, c) and rule-base systems (Kharlamov et al 2017a(Kharlamov et al , b, 2019Horrocks et al 2016), etc. Domain knowledge will be more deeply integrated in data integration (Jiménez-Ruiz 2015; Pinkel et al 2015Pinkel et al , 2018Kalayci et al 2020;Kharlamov et al 2016), data query, reasoning (Thinh et al 2018Ringsquandl et al 2018) and automatic construction of ML pipelines.…”

Section: Tablementioning

confidence: 99%

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

et al. 2022

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Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing processes. In this work, we propose ML pipelines for quality monitoring in Resistance Spot Welding. Previous approaches mostly focused on estimating quality of welding based on data collected from laboratory or experimental settings. Then, they mostly treated welding operations as independent events while welding is a continuous process with a systematic dynamics and production cycles caused by maintenance. Besides, model interpretation based on engineering know-how, which is an important and common practice in manufacturing industry, has mostly been ignored. In this work, we address these three issues by developing a novel feature-engineering based ML approach. Our method was developed on top of real production data. It allows to analyse sequences of welding instances collected from running manufacturing lines. By capturing dependencies across sequences of welding instances, our method allows to predict quality of upcoming welding operations before they happen. Furthermore, in our work we strive to combine the view of engineering and data science by discussing characteristics of welding data that have been little discussed in the literature, by designing sophisticated feature engineering strategies with support of domain knowledge, and by interpreting the results of ML analysis intensively to provide insights for engineering. We developed 12 ML pipelines in two dimensions: settings of feature engineering and ML methods, where we considered 4 feature settings and 3 ML methods (linear regression, multi-layer perception and support vector regression). We extensively evaluated our ML pipelines on data from two running industrial production lines of 27 welding machines with promising results.

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Section: Tablementioning

confidence: 99%

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

et al. 2022

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“…The more widespread use of Knowledge Graphs in the industry (e.g. [134]) or at least industrial research [169] finally shows the applicability of these ideas. However, many projects on ontologies or other forms of context description have remained dry theory and have never been able to prove in practice their usefulness.…”

Section: ) Immaturity Of Applicabilitymentioning

confidence: 99%

Context-Aware Security for Vehicles and Fleets: A Survey

2021

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Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user's life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these everincreasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future.

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“…Indeed, none of the ontologies for manufacturing (e.g., [53][54][55][56][57][58]) fully serve as the communication model for our use cases and sufficiently cover our domains. The mapping-based data integration solutions like Ontop [59] are not particularly targeted towards our aim of minimising the involvement of ontologists into the model maintenance processes. Moreover, the role of mappings in our context is not limited to the transformation of data into the RDF format.…”

Section: Related Workmentioning

confidence: 99%

“…In this work we address the C1-C3 challenges by enhancing machine learning development for condition monitoring with semantic technologies. Note that semantics has recently gained a considerable attention in industry in a wide range of applications and automation tasks such as modelling of industrial assets [12] industrial analytics [13], integration [14][15][16] and querying [17][18][19] of production data, process monitoring [20] and equipment diagnostics [21], moreover, semantic technologies have been adopted or evaluated in a number of large high tech production companies such as Equinor [22], Siemens [23], Festo [24], and Bosch [25,26].…”

Section: Introductionmentioning

confidence: 99%