A flexible architecture for data mining from heterogeneous data sources in automated production systems

Trunzer, Emanuel; Kirchen, Iris; Folmer, Jens; Koltun, Gennadiy; Vogel‐Heuser, Birgit

doi:10.1109/icit.2017.7915517

Cited by 25 publications

(12 citation statements)

References 15 publications

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“…A system architecture should be adoptable for different applications and use-cases of different sizes, which requires a scalable architecture. If the number of architecture participants increases due to the integration of additional tools, an architecture has to scale accordingly [22].…”

Section: General Requirements: Interoperabilitymentioning

confidence: 99%

“…The IMPROVE architecture [22] supports a multitude of different applications and tools. Standard interfaces are introduced to minimize the integration effort.…”

Section: Improve: Data Exchange and Data Qualitymentioning

confidence: 99%

“…In [22], Trunzer et al present the results of expert interviews. These interviews are carried out within the scope of IMPROVE and a second project, SIDAP [36].…”

Section: Improve: Expert Interviews and Prototypical Implementationmentioning

confidence: 99%

“…Through the evaluations carried out in each project, the subfunctionalities under consideration have been successfully verified. The feasibility and applicability of the basic middleware concept and the data adapters has been demonstrated for several use-cases [22,27,35]. Additionally, with PML [29,35] a candidate for a common information model and its integration into the architecture has been successfully evaluated.…”

Section: Generic Architecture For the Factory Of The Futurementioning

confidence: 99%

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System architectures for Industrie 4.0 applications

Trunzer

Calà

Leitão

et al. 2019

Prod. Eng. Res. Devel.

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Industrie 4.0 principles demand increasing flexibility and modularity for automated production systems. Current system architectures provide an isolated view of specific applications and use cases, but lack a global, more generic approach. Based on the specific architectures of two EU projects and one German Industrie 4.0 project, a generic system architecture is proposed. This system architecture features the strengths of the three isolated proposals, such as cross-enterprise data sharing, service orchestration, and real-time capabilities, and can be applied to a wide field of applications. Future research should be directed towards considering the applicability of the architecture to other equal applications.

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Section: General Requirements: Interoperabilitymentioning

confidence: 99%

“…The IMPROVE architecture [22] supports a multitude of different applications and tools. Standard interfaces are introduced to minimize the integration effort.…”

Section: Improve: Data Exchange and Data Qualitymentioning

confidence: 99%

“…In [22], Trunzer et al present the results of expert interviews. These interviews are carried out within the scope of IMPROVE and a second project, SIDAP [36].…”

Section: Improve: Expert Interviews and Prototypical Implementationmentioning

confidence: 99%

Section: Generic Architecture For the Factory Of The Futurementioning

confidence: 99%

See 2 more Smart Citations

System architectures for Industrie 4.0 applications

Trunzer

Calà

Leitão

et al. 2019

Prod. Eng. Res. Devel.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This paper describes a generic architecture that can be applied to various scenarios and shows its concrete use and practicability in a lab-scale production system. It pays special attention to the multitude of requirements arising from automated production systems, legacy systems, heterogeneous sources, and data processing, This contribution is an extended and adapted version of the contribution presented at the 2017 IEEE International Conference on Industrial Technology (ICIT 2017) [7]. In addition to the original version, the literature review is expanded and a prototypical implementation is added.…”

Section: Introductionmentioning

confidence: 99%

Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems

Trunzer

Lötzerich

Vogel‐Heuser

2018

Technologien Für Die Intelligente Automation

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The integration of smart devices into the production process results in the emergence of cyber-physical production systems (CPPSs) that are a key part of Industrie 4.0. Various sensors, actuators, Programmable Logic Controllers (PLCs), Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems produce huge amounts of data and meta data that can hardly be handled by conventional analytic methods. The main goal of this work is to develop an innovative architecture for handling big data from various heterogeneous sources within an automated production system (aPS). Moreover, enabling data analysis to gain a better understanding of the whole process, spotting possible defects in advance and increasing the overall equipment effectiveness (OEE), is in focus. This new architecture vertically connects the production lines to the analysts by using a generic data format for dealing with various types of data. The presented model is applied prototypically to a lab-scale production unit. Based on a message broker, the presented prototype is able to process messages from different sources, using e.g. OPC UA and MQTT protocols, storing them in a database and providing them for live-analysis. Furthermore, data can be anonymized, depending on granted access rights, and can be provided to external analyzers. The prototypical implementation of the architecture is able to operate in a heterogeneous environment supporting many platforms. The prototype is stress tested with different workloads showing hardly any response in the form of longer delivery times. Thus, feasibility of the architecture and its suitability for industrial, near real-time applications can be shown on a labscale.

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Towards artificial intelligence at scale in the chemical industry

et al. 2022

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In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision‐making is shown. Then state‐of‐the‐art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI‐ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.

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A flexible architecture for data mining from heterogeneous data sources in automated production systems

Cited by 25 publications

References 15 publications

System architectures for Industrie 4.0 applications

System architectures for Industrie 4.0 applications

Concept and Implementation of a Software Architecture for Unifying Data Transfer in Automated Production Systems

Towards artificial intelligence at scale in the chemical industry

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