Now showing 1 - 10 of 10
  • Publication
    Open Access
    End-to-end MLOps integration: a case study with ISS telemetry data
    (UB HSU, 2024-03) ;
    Geier, Christian
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    Creutzenberg, Martin
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    Pfeifer, Jann
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    Turk, Samo
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    Kubeflow integrates a suite of powerful tools for Machine Learning (ML) software development and deployment, typically showcased independently. In this study, we integrate these tools within an end- to-end workflow, a perspective not extensively explored previously. Our case study on anomaly detection using telemetry data from the International Space Station (ISS) investigates the integration of various tools—Dask, Katib, PyTorch Operator, and KServe—into a single Kubeflow Pipelines (KFP) workflow. This investigation reveals both the strengths and limitations of such integration in a real-world context. The insights gained from our study provide a comprehensive blueprint for practitioners and contribute valuable feedback for the open source community developing Kubeflow.
  • Publication
    Open Access
    Integrating continuous-time neural networks in engineering: bridging machine learning and dynamical system modeling
    This paper examines the integration of Continuous-Time Neural Networks (CTNNs), including Neural ODEs, CDEs, Neural Laplace, and Neural Flows, into engineering practices, particularly in dynamical system modeling. We provide a detailed introduction to CTNNs, highlighting their underutilization in engineering despite similarities with traditional Ordinary Differential Equation (ODE) models. Through a comparative analysis with conventional engineering approaches, using a spring-mass-damper system as an example, we demonstrate both theoretical and practical aspects of CTNNs in engineering contexts. Our work underscores the potential of CTNNs to harmonize with traditional engineering methods, exploring their applications in Cyber- Physical Systems (CPS). Additionally, we review key open-source software tools for implementing CTNNs, aiming to facilitate their broader integration into engineering practices.
  • Publication
    Open Access
    Towards the generation of models for fault diagnosis of CPS using VQA models
    (UB HSU, 2024-03)
    Merkelbach, Silke
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    Enzberg, Sebastian von
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    Dumitrescu, Roman
    In many use cases cyber-physical systems are employed to produce products of small batch sizes as efficiently as possible. From an engineering standpoint, a major drawback of this flexibility is that the architecture of the cyber-physical system may change multiple times over its lifetime to accommodate new product variants. To keep a cyber-physical system working normally it has become common to employ fault diagnosis algorithms. These algorithms partly rely on physical first-principles models that need to be updated when the architecture of the system changes which usually has to be done manually. In this article we present a practical approach to obtain such a first-principles model through evaluating piping and instrumentation diagrams (P&IDs) with visual questions answering (VQA) models. We demonstrate that it is possible to leverage VQA models to construct physical equations which are a preliminary stage for the creation of models suitable for fault diagnosis. We evaluate our approach on OpenAIs GPT-4 Vision Preview model using a P&ID we created for a benchmark water tank system. Our results show that VQA models can be used to create physical first-principles models.
  • Publication
    Metadata only
    Anomaly Detection with Autoencoders as a Tool for Detecting Sensor Malfunctions
    One possibility to extend the service life of engi-neering structures is to provide adequate maintenance based on Structural Health Monitoring (SHM). Typically, SHM involves a sensor network which is spatially distributed at the surface or within the structure to be monitored. Each sensor measures at least one physical quantity, the data of all sensors then have to be properly evaluated to derive the health state and to predict the remaining service life. Health issues may be detected by machine learning methods by looking for anomalous behaviour in sensor data. Hereby the problem is that malfunctions differ excessively in the representation of the data collected by sensors such that specialisation of methods on anomaly types is required. The current contribution suggests the simulation of sensor malfunction based on established criteria by creating different types of artificial anomalous data indicating different types of issues. Several proposed autoencoder approaches are verified for different anomaly representations, which are artificially introduced in a set of data. The final solutions are different autoencoder specialized on different types of simulated anomaly data, making the conclusions drawn from the measured data more reliable. As a case study, data of a numerical experiment of fibre pull-out are considered.
  • Publication
    Metadata only
    A Research Agenda for AI Planning in the Field of Flexible Production Systems
    Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived.
  • Publication
    Metadata only
    Differential Evolution in Production Process Optimization of Cyber Physical Systems
    (Springer Vieweg, 2020)
    Giese, Katharina
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    Eickmeyer, Jens
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  • Publication
    Metadata only
  • Publication
    Metadata only
    Improved Domain Modeling for Realistic Automated Planning and Scheduling in Discrete Manufacturing
    (IEEE, 2018-10-22)
    Rogalla, Antje
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    Current production planning and scheduling systems in automation do not meet the requirements of modern individualized production. Today's, static production processes impede customized manufacturing and small-scale production. A new way of thinking towards a dynamic control is required. This paper focuses on automated integrated process planning and scheduling on control level in discrete manufacturing. Existing algorithms in artificial intelligence planning are applied to solve process planning and scheduling problems. The challenge is to model the manufacturing system and products in a way that automated planners can generate efficiently process plans and schedules. Hence, based on a general classification of operations, different modeling options with regard to a successful automated process planning and scheduling are discussed. As a result, a domain modeling approach for discrete manufacturing is presented.