arXiv subjects

Felix Gehlhoff

Publications and source records attributed to Felix Gehlhoff.

At least 37 records · Page 2Linked to original sources

arXiv2025

Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL

AutomationML (AML) enables standardized data exchange in engineering, yet existing recommendations for proper AML modeling are typically formulated as informal and textual constraints. These constraints cannot be validated automatically within AML itself. This work-in-progress paper introduces a pipeline to formalize and verify such constraints. First, AML models are mapped to OWL ontologies via RML and SPARQL. In addition, a Large Language Model translates textual rules into SHACL constraints, which are then validated against the previously generated AML ontology. Finally, SHACL validation results are automatically interpreted in natural language. The approach is demonstrated on a sample AML recommendation. Results show that even complex modeling rules can be semi-automatically checked -- without requiring users to understand formal methods or ontology technologies.

cs.AI↗

arXiv2025

Integrating AI Planning Semantics into SysML System Models for Automated PDDL File Generation

This paper presents a SysML profile that enables the direct integration of planning semantics based on the Planning Domain Definition Language (PDDL) into system models. Reusable stereotypes are defined for key PDDL concepts such as types, predicates, functions and actions, while formal OCL constraints ensure syntactic consistency. The profile was derived from the Backus-Naur Form (BNF) definition of PDDL 3.1 to align with SysML modeling practices. A case study from aircraft manufacturing demonstrates the application of the profile: a robotic system with interchangeable end effectors is modeled and enriched to generate both domain and problem descriptions in PDDL format. These are used as input to a PDDL solver to derive optimized execution plans. The approach supports automated and model-based generation of planning descriptions and provides a reusable bridge between system modeling and AI planning in engineering design.

cs.AI↗

arXiv2025

Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants

Advances in Automation and Artificial Intelligence continue to enhance the autonomy of process plants in handling various operational scenarios. However, certain tasks, such as fault handling, remain challenging, as they rely heavily on human expertise. This highlights the need for systematic, knowledge-based methods. To address this gap, we propose a methodological framework that integrates Large Language Model (LLM) agents with a Digital Twin environment. The LLM agents continuously interpret system states and initiate control actions, including responses to unexpected faults, with the goal of returning the system to normal operation. In this context, the Digital Twin acts both as a structured repository of plant-specific engineering knowledge for agent prompting and as a simulation platform for the systematic validation and verification of the generated corrective control actions. The evaluation using a mixing module of a process plant demonstrates that the proposed framework is capable not only of autonomously controlling the mixing module, but also of generating effective corrective actions to mitigate a pipe clogging with only a few reprompts.

cs.AI↗

arXiv2024

Nutzung von Massespeichern zur Flexibilisierung des Energieverbrauchs: Kosteneffizienter Anlagenbetrieb durch Anpassung an Marktpreise

The increasing share of renewable energy sources and necessitate new concepts for energy flexible operation of industrial production resources. In this paper, we demonstrate the potential of mass storage to increase energy flexibility in industrial operations through the application of optimized operational planning based on market prices. A wastewater treatment plant equipped with decanters and storages is examined to optimally utilize its energy-flexibility. An MILP model was created for energy-flexible production facilities. The resulting operation plan was transmitted to the plant operators and executed over a period of more than 24 hours. The model shows good agreement with real measurements (average errors between 3% to 7%). The results demonstrate significant potential for cost savings of roughly 56% for the investigated time horizon. -- Der zunehmende Anteil erneuerbarer Energien erfordert neue Konzepte zur energieflexiblen Steuerung industrieller Produktionsanlagen. In diesem Beitrag wird demonstriert, wie das Potenzial von Massespeichern zur Steigerung der Energieflexibilität in industriellen Prozessen durch die Anwendung optimierter Betriebsplanung basierend auf Marktpreisen, genutzt werden kann. Es wird eine Abwasseraufbereitungsanlage mit Dekantern und zugehörigen Massespeichersystemen betrachtet, um deren Energieflexibilität optimiert zu nutzen. Der resultierende Betriebsplan wurde in einem Assistenzsystem an die Anlagenbetreiber übermittelt und über einen Zeitraum von über 24 Stunden ausgeführt. Das Modell zeigt eine gute Übereinstimmung mit den realen Messwerten (Fehlern zwischen 3% und 7%). Alle von den Betreibern gesetzten Betriebsziele wurden erreicht, was die Anwendbarkeit des Modells in industriellen Umgebungen bestätigt. Die Ergebnisse zeigen ein erhebliches Einsparpotenzial von etwa 56% für den untersuchten Zeitraum.

eess.SY↗

arXiv2024

Semantic Capability Model for the Simulation of Manufacturing Processes

Simulations offer opportunities in the examination of manufacturing processes. They represent various aspects of the production process and the associated production systems. However, often a single simulation does not suffice to provide a comprehensive understanding of specific process settings. Instead, a combination of different simulations is necessary when the outputs of one simulation serve as the input parameters for another, resulting in a sequence of simulations. Manual planning of simulation sequences is a demanding task that requires careful evaluation of factors like time, cost, and result quality to choose the best simulation scenario for a given inquiry. In this paper, an information model is introduced, which represents simulations, their capabilities to generate certain knowledge, and their respective quality criteria. The information model is designed to provide the foundation for automatically generating simulation sequences. The model is implemented as an extendable and adaptable ontology. It utilizes Ontology Design Patterns based on established industrial standards to enhance interoperability and reusability. To demonstrate the practicality of this information model, an application example is provided. This example serves to illustrate the model's capacity in a real-world context, thereby validating its utility and potential for future applications.

cs.SE↗

arXiv2024

Exploring LLMs for Verifying Technical System Specifications Against Requirements

Requirements engineering is a knowledge intensive process and crucial for the success of engineering projects. The field of knowledge-based requirements engineering (KBRE) aims to support engineers by providing knowledge to assist in the elicitation, validation, and management of system requirements. The advent of large language models (LLMs) opens new opportunities in the field of KBRE. This work experimentally investigates the potential of LLMs in requirements verification. Therein, LLMs are provided with a set of requirements and a textual system specification and are prompted to assess which requirements are fulfilled by the system specification. Different experimental variables such as system specification complexity, the number of requirements, and prompting strategies were analyzed. Formal rule-based systems serve as a benchmark to compare LLM performance to. Requirements and system specifications are derived from the smart-grid domain. Results show that advanced LLMs, like GPT-4o and Claude 3.5 Sonnet, achieved f1-scores between 79 % and 94 % in identifying non-fulfilled requirements, indicating potential for LLMs to be leveraged for requirements verification.

cs.SE↗

arXiv2024

Prävention und Beseitigung von Fehlerursachen im Kontext von unbemannten Fahrzeugen

Mobile robots, becoming increasingly autonomous, are capable of operating in diverse and unknown environments. This flexibility allows them to fulfill goals independently and adapting their actions dynamically without rigidly predefined control codes. However, their autonomous behavior complicates guaranteeing safety and reliability due to the limited influence of a human operator to accurately supervise and verify each robot's actions. To ensure autonomous mobile robot's safety and reliability, which are aspects of dependability, methods are needed both in the planning and execution of missions for autonomous mobile robots. In this article, a twofold approach is presented that ensures fault removal in the context of mission planning and fault prevention during mission execution for autonomous mobile robots. First, the approach consists of a concept based on formal verification applied during the planning phase of missions. Second, the approach consists of a rule-based concept applied during mission execution. A use case applying the approach is presented, discussing how the two concepts complement each other and what contribution they make to certain aspects of dependability.

cs.RO↗

arXiv2024

On the Use of Large Language Models to Generate Capability Ontologies

Capability ontologies are increasingly used to model functionalities of systems or machines. The creation of such ontological models with all properties and constraints of capabilities is very complex and can only be done by ontology experts. However, Large Language Models (LLMs) have shown that they can generate machine-interpretable models from natural language text input and thus support engineers / ontology experts. Therefore, this paper investigates how LLMs can be used to create capability ontologies. We present a study with a series of experiments in which capabilities with varying complexities are generated using different prompting techniques and with different LLMs. Errors in the generated ontologies are recorded and compared. To analyze the quality of the generated ontologies, a semi-automated approach based on RDF syntax checking, OWL reasoning, and SHACL constraints is used. The results of this study are very promising because even for complex capabilities, the generated ontologies are almost free of errors.

cs.AI↗

arXiv2024

Toward a Method to Generate Capability Ontologies from Natural Language Descriptions

To achieve a flexible and adaptable system, capability ontologies are increasingly leveraged to describe functions in a machine-interpretable way. However, modeling such complex ontological descriptions is still a manual and error-prone task that requires a significant amount of effort and ontology expertise. This contribution presents an innovative method to automate capability ontology modeling using Large Language Models (LLMs), which have proven to be well suited for such tasks. Our approach requires only a natural language description of a capability, which is then automatically inserted into a predefined prompt using a few-shot prompting technique. After prompting an LLM, the resulting capability ontology is automatically verified through various steps in a loop with the LLM to check the overall correctness of the capability ontology. First, a syntax check is performed, then a check for contradictions, and finally a check for hallucinations and missing ontology elements. Our method greatly reduces manual effort, as only the initial natural language description and a final human review and possible correction are necessary, thereby streamlining the capability ontology generation process.

cs.AI↗

arXiv2024

Chatbot-Based Ontology Interaction Using Large Language Models and Domain-Specific Standards

The following contribution introduces a concept that employs Large Language Models (LLMs) and a chatbot interface to enhance SPARQL query generation for ontologies, thereby facilitating intuitive access to formalized knowledge. Utilizing natural language inputs, the system converts user inquiries into accurate SPARQL queries that strictly query the factual content of the ontology, effectively preventing misinformation or fabrication by the LLM. To enhance the quality and precision of outcomes, additional textual information from established domain-specific standards is integrated into the ontology for precise descriptions of its concepts and relationships. An experimental study assesses the accuracy of generated SPARQL queries, revealing significant benefits of using LLMs for querying ontologies and highlighting areas for future research.

cs.IR↗

arXiv2024

Semantic model for the description of energy data in the Module Type Package

Modular production systems that employ the Module Type Package (MTP) to describe module interfaces can, at present, only communicate energy data through proprietary solutions. Due to this limitation, users face additional effort when calculating energy KPIs for modules or determining the energy efficiency of modules. To address this issue, we present a model that facilitates energy data to be described semantically and uniformly in the MTP on the basis of an industrial standard (OPC 34100). MTPs incorporating this model can transmit semantically consistent energy data from modules to the process control system, making the data available for further applications, such as monitoring or optimization.

eess.SY↗

arXiv2024

Model-based Workflow for the Automated Generation of PDDL Descriptions

Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore, this contribution presents a comprehensive workflow for the automated generation of PDDL descriptions from integrated system and product models. The proposed workflow leverages Model-Based Systems Engineering (MBSE) to organize and manage system and product information, translating it automatically into PDDL syntax for planning purposes. By connecting system and product models with planning aspects, it ensures that changes in these models are quickly reflected in updated PDDL descriptions, facilitating efficient and adaptable planning processes. The workflow is validated within a use case from aircraft assembly.

cs.AI↗

arXiv2024

Combination of Site-Wide and Real-Time Optimization for the Control of Systems of Electrolyzers

The rapid expansion of renewable energy sources has introduced significant volatility and unpredictability in the energy supply chain, necessitating advanced control strategies to ensure grid stability and reliability. Green hydrogen production via electrolysis offers a viable solution for converting and storing this volatile renewable energy. However, the inherent fluctuations of renewable energy sources present challenges for consistent utilization and integration of green hydrogen. This work proposes a two-stage optimization approach, combining site-wide optimization and real-time optimization for managing systems of electrolyzers. By adapting an existing static optimization model, dual use is achieved in both site-wide optimization and real-time optimization. The hierarchical optimization structure, characterized by distinct temporal resolutions, enables effective responses to both dynamic changes and long-term trends. The side-wide optimization layer generates long-term plans based on forecast data, while the real-time optimization layer refines these plans in real-time, accommodating immediate fluctuations and ensuring efficient operation. The results from the case study on a system of electrolyzers demonstrate the method's effectiveness in aligning electrolyzer operation with actual availability of renewable energy. This approach offers a robust framework for optimizing the operation of electrolyzers but also other types of flexible energy resources, contributing to sustainable and economically viable energy management.

math.OC↗

arXiv2024

Integrating Ontology Design with the CRISP-DM in the context of Cyber-Physical Systems Maintenance

In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber-Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant.

cs.AI↗

arXiv2024

A Formal Model for Artificial Intelligence Applications in Automation Systems

The integration of Artificial Intelligence (AI) into automation systems has the potential to enhance efficiency and to address currently unsolved existing technical challenges. However, the industry-wide adoption of AI is hindered by the lack of standardized documentation for the complex compositions of automation systems, AI software, production hardware, and their interdependencies. This paper proposes a formal model using standards and ontologies to provide clear and structured documentation of AI applications in automation systems. The proposed information model for artificial intelligence in automation systems (AIAS) utilizes ontology design patterns to map and link various aspects of automation systems and AI software. Validated through a practical example, the model demonstrates its effectiveness in improving documentation practices and aiding the sustainable implementation of AI in industrial settings.

eess.SY↗

arXiv2024

A SysML Profile for the Standardized Description of Processes during System Development

A key aspect in creating models of production systems with the use of model-based systems engineering (MBSE) lies in the description of system functions. These functions shouldbe described in a clear and standardized manner.The VDI/VDE 3682 standard for Formalised Process De-scription (FPD) provides a simple and easily understandable representation of processes. These processes can be conceptualized as functions within the system model, making the FPD particularly well-suited for the standardized representation ofthe required functions. Hence, this contribution focuses on thedevelopment of a Domain-Specific Modeling Language(DSML) that facilitates the integration of VDI/VDE 3682 into the Systems Modeling Language (SysML). The presented approach not onlyextends classical SysML with domain-specific requirements but also facilitates model verification through constraints modeled in Object Constraint Language (OCL). Additionally, it enables automatic serialization of process descriptions into the Extensible Markup Language (XML) using the Velocity Template Language (VTL). This serialization enables the use of process modeling in applications outside of MBSE. The approach was validated using an collar screwing use case in the major component assembly in aircraft production.

cs.SE↗

arXiv2024

Cost Optimized Scheduling in Modular Electrolysis Plants

In response to the global shift towards renewable energy resources, the production of green hydrogen through electrolysis is emerging as a promising solution. Modular electrolysis plants, designed for flexibility and scalability, offer a dynamic response to the increasing demand for hydrogen while accommodating the fluctuations inherent in renewable energy sources. However, optimizing their operation is challenging, especially when a large number of electrolysis modules needs to be coordinated, each with potentially different characteristics. To address these challenges, this paper presents a decentralized scheduling model to optimize the operation of modular electrolysis plants using the Alternating Direction Method of Multipliers. The model aims to balance hydrogen production with fluctuating demand, to minimize the marginal Levelized Cost of Hydrogen (mLCOH), and to ensure adaptability to operational disturbances. A case study validates the accuracy of the model in calculating mLCOH values under nominal load conditions and demonstrates its responsiveness to dynamic changes, such as electrolyzer module malfunctions and scale-up scenarios.

eess.SY↗

arXiv2023

Systematic Comparison of Software Agents and Digital Twins: Differences, Similarities, and Synergies in Industrial Production

To achieve a highly agile and flexible production, it is envisioned that industrial production systems gradually become more decentralized, interconnected, and intelligent. Within this vision, production assets collaborate with each other, exhibiting a high degree of autonomy. Furthermore, knowledge about individual production assets is readily available throughout their entire life-cycles. To realize this vision, adequate use of information technology is required. Two commonly applied software paradigms in this context are Software Agents (referred to as Agents) and Digital Twins (DTs). This work presents a systematic comparison of Agents and DTs in industrial applications. The goal of the study is to determine the differences, similarities, and potential synergies between the two paradigms. The comparison is based on the purposes for which Agents and DTs are applied, the properties and capabilities exhibited by these software paradigms, and how they can be allocated within the Reference Architecture Model Industry 4.0. The comparison reveals that Agents are commonly employed in the collaborative planning and execution of production processes, while DTs typically play a more passive role in monitoring production resources and processing information. Although these observations imply characteristic sets of capabilities and properties for both Agents and DTs, a clear and definitive distinction between the two paradigms cannot be made. Instead, the analysis indicates that production assets utilizing a combination of Agents and DTs would demonstrate high degrees of intelligence, autonomy, sociability, and fidelity. To achieve this, further standardization is required, particularly in the field of DTs.

cs.SE↗