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Semantic models in process monitoring: Semantic models for field device performance monitoring in the process industry in the context of industry 4.0

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VDI Berichte 2023年第2419期2023卷 233-244页

作者： Hana, Ramy Kleinert, Tobias Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen University Aachen Germany

The introduction of numerous semantic models (e.g. Asset administration shells (AAS) and automationML (AML) models) has facilitated the description of different aspects of production in the process industry. Semantic models provide a flexible structure for encapsulating annotated information. The models are integrated with standardized data exchange formats (e.g. XML) which enables a wide range of automated functions. One of these functions would be field device performance assessment where the different semantic models can help in creating an assessment ecosystem for the continuous monitoring of field devices. © 2023 The Authors.

关键词： Flexible structures

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Maturity Evaluation of SDKs for I4.0 Digital Twins 28

Maturity Evaluation of SDKs for I4.0 Digital Twins

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Braunisch, Nico Lehmann, Robert Wollschlaeger, Martin Ristin, Marko Van De Venn, Hans Wernher Otto, Björn Kleinert, Tobias TU Dresden Institute of Applied Computer Science Dresden Germany Institute of Mechatronic Systems Zurich Switzerland Otto von Guericke University Institute for Automation & Communication Magdeburg Germany RWTH Aachen University Information and Automation Systems for Process and Material Technology Aachen Germany

ISBN: (纸本)9798350339918

Digital twins, the virtual representations of physical assets, processes or systems, are becoming increasingly important in cyber-physical systems. They are a foundational block of Industry 4.0, the movement to digitalize industrial *** Asset Administration Shell (AAS) has been established as the preferable model to capture interoperable digital twins in the context of Industry 4.0. As asset administration shells become more prevalent, so does the need for specialized software development kits (SDKs) to manage *** intertwined Industry 4.0 value chains are highly dependent on interoperability. The SDKs for AAS are often the direct interface between the components, and the malfunctioning of the SDKs leads to the breakage of the value chain. It is therefore important that we have the tools to determine how individual SDKs behave, and detect when they malfunction. In this work, we implement an approach to assessing the maturity of the SDKs for managing asset administration shells, and perform a thorough survey and evaluation of the existing SDKs. © 2023 IEEE.

关键词： Shells (structures)

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A Flow Graph based Approach for controlled Generation of AAS Digital Twin Instances for the Verification of Compliance Check Tools 48

A Flow Graph based Approach for controlled Generation of AAS...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Otto, Bjorn Kleinert, Tobias Institute for Automation and Communication Otto von Guericke University Magdeburg Germany Rwth Aachen University Chair of Information and Automation Systems for Process and Material Technology Aachen Germany

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Digital twins are a fundamental building block for the Industrie 4.0. With the Asset Administration Shell, a standard has been published that describes a meta model of such digital twins and the interfaces to access them. One of these interfaces is file-based access. Since the advent of the standard, various implementations have been developed. To ensure interoperability between different implementations, compliance with the specification must be verified. To test the correctness of a compliance check tool, a set of digital twins with known properties is used as test input. Since manually specifying such test models is error-prone, this work contributes an automated generation approach. It leverages flow graphs to represent the space of possible models. The subsequent path selection aims to generate expressive, yet efficient test input. © 2022 IEEE.

关键词： Graphic methods

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Automatic Generation of Submodel-Specific Classes with Predefined Meta-information Based on Submodel Templates

Automatic Generation of Submodel-Specific Classes with Prede...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Igor Garmaev Torben Miny Tobias Kleinert Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen Aachen Germany

Asset Administration Shells are an essential component of Industry 4.0, and submodels are crucial to their functioning. However, the current method of generating submodels involves writing custom code, which can lead to code duplication and inconsistencies across submodels. To address these challenges, we propose an automatic generation of submodel-specific classes with predefined meta-information based on submodel templates. Our proposed solution aims to reduce code duplication, provide support in integrated development environments, ensure consistency across submodels, and facilitate the immediate publishing of submodel software development kits. This paper discusses the current approaches for instantiating submodels and their problems, the proposed approach, its requirements, advantages, and an outlook.

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Standardized Integration of Source systems into Asset Administration Shell Realizations

Standardized Integration of Source Systems into Asset Admini...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Torben Miny Sebastian Heppner Wei Guo Leon Möller Tobias Kleinert Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen University Aachen Germany

The Asset Administration Shell is the key concept for the exchange of asset information in Industrie 4.0 across the entire life cycle of an asset. Therefore, the Asset Administration Shell acts as the central interface to access asset information, which is stored in different source systems, e.g. databases or control units. To achieve this vision, this paper presents the requirements for the standardized integration of data from these source systems into Asset Administration Shell applications and a generic concept for easy and vendor-independent applicability. The concept is demonstrated with two example implementations and evaluated against the requirements.

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Fences: Systematic Sample Generation for JSON Schemas using Boolean Algebra and Flow Graphs

Fences: Systematic Sample Generation for JSON Schemas using ...

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International Workshop on automation of Software Test (AST)

作者： Björn Otto Tobias Kleinert Institute for Automation and Communication Otto von Guericke University Germany Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen University Germany

ISBN: (数字)9798400705885

ISBN: (纸本)9798350352184

In this work, we present Fences, a tool to generate sample data for arbitrary JSON Schemas. Fences generates these samples in three basic steps: First, the schema is simplified and normalized using boolean algebra. Second, the schema is mapped to a flow graph. And third, the flow graph is analyzed to obtain the final sets of JSON data. Besides valid samples, Fences also generates invalid samples it knows to be rejected by the schema. By combining these sets, all degrees of freedom offered by the schema are covered systematically. We assess the feasibility and correctness of our approach using synthetic schemas from the official JSON Schema test suite and a real-world use case from the Industry 4.0 domain. Additionally, we introduce schema coverage as metric to assess the completeness of our approach.

关键词： Measurement Systematics Software Boolean algebra Fourth Industrial Revolution Flow graphs

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Detection of Novel Asset Failures in Predictive Maintenance Using Classifier Certainty

Detection of Novel Asset Failures in Predictive Maintenance ...

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Mediterranean Conference on Control and automation (MED)

作者： Kiavash Fathi Marko Ristin Marcin Sadurski Tobias Kleinert Hans Wernher van de Venn Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen University Aachen Germany Institute of Mechatronic Systems Zurich University of Applied Sciences Winterthur Switzerland

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Model reliability is one of the main building blocks of trustworthy AI where models are capable of indicating their prediction certainty given the provided input data. Despite the numerous model calibration techniques available to gauge the reliability of trained models, it has been shown that even well-calibrated classification models cannot properly represent their uncertainty for data points outside the training distribution. Such models, therefore, cannot be used in industrial settings to find previously unidentified failures in an asset, leaving the maintenance crews in the dark. To counter that, we analyze the sequence of the predicted probability values from a condition monitoring classification sub-model of a predictive maintenance solution. We demonstrate that the data points from unknown regions of the asset parameter space can be flagged by simply analyzing such sequences. The flagged failure instances of the asset are consequently sent for thorough inspection to the maintenance crew. Furthermore, we demonstrate on a practical real-world scenario the effectiveness of our proposed method (mean accuracy of 94.35 %) by comparing it with models trained only on pre-processed data (mean accuracy of 27.96 %) or models based on autoencoder reconstruction error (mean accuracy of 51.09%). We finally show how the predicted probability values from different regions of the parameter space exhibit a higher degree of separability between normal working condition, known and unknown failure modes compared to signal readings of the system or reconstruction errors of an anomaly detection model.

关键词： Employee welfare Training Analytical models Accuracy Uncertainty Predictive models Reliability engineering

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Sustainability in Semiconductor Production via Interpretable and Reliable Predictions ⁎

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IFAC-PapersOnLine 2024年第4期58卷 174-179页

作者： Kiavash Fathi Maria Stramaglia Marko Ristin Marcin Sadurski Tobias Kleinert Robert Schönfelder Hans Wernher van de Venn Zurich University of Applied Sciences 8400 Winterthur Switzerland Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen University 52064 Aachen Germany Hitachi Energy Semiconductors 5600 Lenzburg Switzerland

Sustainability in production stands out as one of the foremost achievements facilitated by Industry 4.0. With the continuous monitoring of production lines, it is now possible to detect quality deviations at their earliest occurrence and reduce the scrap production rate. This paper studies the sustainability in context of a partially monitored multistage semiconductor production line with multiple test units. Our goal is to provide a foundation for interpretable and reliable Product Quality Prediction (PQP) in industrial use cases with noisy quality check results and missing process information. To that end, we examine how the accumulated process information from different steps of the production line impacts the accuracy of the PQP models used later as base learners. Furthermore, we highlight the drawbacks of conventional model stacking on the accuracy due to base learner’s prediction quantization. We propose instead an interpretable stacked model (SM) from base learners’ predicted probability values, and demonstrate how it increases the accuracy and reliability of the predictions. To improve the results even further, we analyze different calibration methods both for base learners and the SM. Our final model leads to a 19.49% reduction in the binary estimated calibration error compared to the conventional models, and thus allows for increased PQP reliability.

关键词： Model reliability Interpretable AI Quality prediction Industry 4.0

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Formulation of a Master Recipe: A Capability Aligning Approach for Resource Allocation

Formulation of a Master Recipe: A Capability Aligning Approa...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Michael Winter Tobias Klausmann Shagufta Shagufta Torben Miny Sebastian Heppner Tobias Kleinert Information and Automation Systems for Process and Material Technology RWTH Aachen University Aachen Germany Research & Development — Systems Engineering Lenze SE Aerzen Germany

The contribution addresses the challenge of transforming a general recipe, which lacks knowledge about specific production resources, into a plant-specific master recipe in the process industry. The authors propose an approach using the concept of capabilities in manufacturing, enabled by an ontology and the Asset Administration Shell. The approach aligns required and offered capabilities at different description levels to allocate equipment to process elements and generate a master recipe in BatchML. The validation of the approach is accompanied by a procedure logic for a modular plant and discusses open challenges. In conclusion, the provided approach offers a methodical and less experience-dependent derivation of a master recipe for a manufacturing process.

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A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial processes Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 722-733页

作者： Li, Dong Liu, Yiqi Huang, Daoping Xu, Chong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Technical University of Denmark Process and Systems Engineering Center Department of Chemical and Biochemical Engineering Lyngby2800 Denmark South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Gannan Normal University School of Physics and Electronic Information Ganzhou341000 China

To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors are usually unacceptable once suffering from high-nonlinear, high-dimension, and imblance data issues. Therefore, a semisupervised soft-sensor, which is learned by a just-in-time method with structure entropy clustering (SS-JITL-SEC), is proposed aiming to improve prediction performance with a simpler way. Inspired by a divide and conquer strategy, a novel SEC method is proposed to achieve several clusters and then to translate the highly complex and nonlinear modeling problems into simple and linear ones. Moreover, the training dataset is extended through a mixed SS labeling approach. Finally, dissimilarity-based JITL works together with the resulting clustering subdatasets to formulate a local adaptive prediction model. Two datasets from different types of wastewater treatment plants are used to verify the effectiveness of the proposed soft-sensor. The results show that the SS-JITL-SEC soft-sensor can achieve better prediction performance than other standard counterparts, and even for effective process monitoring with the resulted residuals. © 2020 IEEE.

关键词： Entropy

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