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

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

ISBN: (纸本)9798350339918

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. © 2023 IEEE.

关键词： Ontology

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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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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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Source Component Shift Detection & Classification for Improved Remaining Useful Life Estimation in Alarm-Based Predictive Maintenance

Source Component Shift Detection & Classification for Improv...

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International Conference on Control, automation and systems ( ICCAS)

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

Predicting the remaining useful life (RUL) plays a significant role in reducing the downtime of operational assets and maintenance costs. While various predictive maintenance (PdM) studies have utilized sensor data to calculate RUL, valuable information from system alarms has often been overlooked or marginally used. This paper introduces the first-ever solely alarm-based PdM model for RUL estimation. To demonstrate the effectiveness of the proposed method in real-world production lines, the alarm data from two instances of milling machines, used for producing artificial bone joints, are utilized for PdM model development and testing. Additionally, given the varying operation hours of the machines and also the changes in the size of the produced bone joints, this study demonstrates the importance of pinpointing different subpopulations of the alarm data, called data modality (DM), for improved RUL estimation. As flexible and adaptable manufacturing required in Industry 4.0 results in constant changes in the distribution of the gathered data, this paper presents a systematic and generic approach for identifying different DMs, which results in a significant decrease of up to 25.20% in the mean absolute error (MAE) for RUL predictions on the whole test dataset, and up to 60.50% in the MAE of the RUL predictions for the minority DM in the test dataset.

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Alarm Flood Clustering improves Cause and Effect Analysis of process Alarm Data

Alarm Flood Clustering improves Cause and Effect Analysis of...

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European Control Conference (ECC)

作者： Adrian Caspari Maximilian F. Thiedemann Paul L. Bussmann Thomas Leifeld Wan Li Nils Richter Daniel A. Vey Yuanchen Zhao Tobias Kleinert BASF SE Center of Expertise Automation Technology GET/EA Ludwigshafen Germany Chair of Information and Automation Systems for Process and Material Technology RWTH Aachen University Turmstr. 46 Aachen Germany

Cause and effect analyses can reveal useful insights about the information flow within process alarm data, which can, in turn, be used to improve the process performance. Cause and effect analyses require selecting suitable time windows of the historical alarm data, as performing the analysis on the entire time horizon or unsuitable windows might negatively influence the analysis due to noise. This motivates the combination of alarm flood identification, clustering, and cause and effect analysis. Hence, we propose to combine a causal inference analysis based on Direct Transfer Entropy (DTE) with alarm flood detection and clustering to perform the DTE analysis on reasonable time intervals. Our approach performs an alarm data clustering first to find intervals of clusters of similar alarm floods. Afterward, we calculate the DTEs on the found alarm flood cluster intervals. Our results show that the combination of clustering and cause and effect analysis reveals results that were not feasible before. This facilitates analyzing the causal relations in the alarm data. The approach reduces the noisy influence of random alarms on the cause and effect analysis, and provides a clearer structure of the causal relations in the alarm data.

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