Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

Filters

7
5 2
Reset filters

Settings
Now showing 1 - 7 of 7
  • Publication
    PowerGrasp - Design and evaluation of a modular soft-robotic arm exosuit for industrial applications
    ( 2020)
    Goppold, Jean-Paul
    ;
    Kuschan, Jan
    ;
    Thiele, Gregor
    ;
    Schmidt, H.
    ;
    Krüger, Jörg
    ;
    Hackbart, R.
    ;
    Kostelnik, J.
    ;
    Liebach, J.
    ;
    Wolschke, M.
    Absence from work caused by overloading the musculoskeletal system lowers the life quality of the worker and gains unnecessary costs for both the employer and the health system. Classical (rigid link) body-worn exoskeletons can help to reduce critical loading but show many disadvantages, preventing exoskeletons from extensive use in industrial environment. The presented PowerGrasp system is a very robust modular softrobotic arm exosuit sting of robust fabric with embedded rubber tubes as pressure chambers and soft-electronics and who's design is capable to overcome the critical limiting factors of classical exoskeletons. By inflating the tubes via pressure-control valves, it is possible to vary the stiffness of the chambers, which can be effectively used to generate assisting forces and moments at human joints. By using a joint based pressure control, it is possible to decrease the physical demand of overhead working for the wearer. Although the system is designed for i ndustrial overhead assembly, it can also be used in rehabilitation, craftsmanship and construction due to its portable and stand-alone concept. For assessing the impact of the PowerGrasp system, the raise of about 50 percent was shown. Finally, an evaluation study of the overall system has been conducted, showing very high user acceptance and usability.
  • Publication
    System identification of a hysteresis-controlled pump system using SINDy
    ( 2020)
    Thiele, Gregor
    ;
    Fey, Arne
    ;
    Sommer, David
    ;
    Krüger, Jörg
    Hysteresis-controlled devices are widely used in industrial applications. For example, cooling devices usually contain a two-point controller, resulting in a nonlinear hybrid system with two discrete states. Dynamic models of systems are essential for optimizing such industrial supply technology. However, conventional system identification approaches can hardly handle hysteresis-controlled devices. Thus, the new identification method Sparse Identification of Nonlinear Dynamics (SINDy) is extended to consider hybrid systems. SINDy composes models from basis functions out of a customized library in a data-driven manner. For modeling systems that behave dependent on their own past as in the case of natural hysteresis, Ferenc Preisach introduced the relay hysteron as an elementary mathematical description. In this new method (SINDyHybrid), tailored basis functions in form of relay hysterons are added to the library which is used by SINDy. Experiments with a hysteresis controlled water basin show that this approach correctly identifies state transitions of hybrid systems and also succeeds in modeling the dynamics of the discrete system states. A novel proximity hysteron achieves the robustness of this method. The impacts of the sampling rate and the signal noise ratio of the measurement data are examined accordingly.
  • Publication
    Process data based Anomaly detection in distributed energy generation using Neural Networks
    ( 2020)
    Klein, Max
    ;
    Thiele, Gregor
    ;
    Fono, Adalbert
    ;
    Khorsandi, Niloufar
    ;
    Schade, David
    ;
    Krüger, Jörg
    The increasing share of renewable energies in the total energy supply includes a growing number of small, decentralized energy generation which also provides control energy. These decentralized stations are usually combined to a virtual power plant which takes over the monitoring and control of the individual participants via an Internet connection. This high degree of automation and the large number of frequently changing subscribers creates new challenges in terms of detecting anomalies. Quickly adaptable, variable and reliable methods of anomaly detection are required. This paper compares two approaches using Neural Networks (NN) with respect to their ability to detect anomalous behavior in real process data of a combined heat and power plant. In order to include process dynamics, one approach includes specifically engineered features, while the other approach uses Long-Short-Term-Memory (LSTM). Both approaches are able to detect rudimentary anomalies. For more demanding anomalies, the respective strengths and weaknesses of the two approaches become apparent.
  • Publication
    Automated continuous learn and improvement process of energy efficiency in manufacturing
    ( 2020)
    Can, Alperen
    ;
    Fisch, Jessica
    ;
    Stephan, Philipp
    ;
    Thiele, Gregor
    ;
    Krüger, Jörg
    Optimizing the energy efficiency of machine tools automatically is promising. There are several metrics to be considered when it comes to automated optimization approaches in serial production which are especially quality, technical availability, and cycle time. These are not supposed to be impaired whereas they are indicated as a central obstacle. The measurements and the machine data show the actions happening in the machine which also leads to the data-driven traceability of machine states. This article presents a method to formulate the necessary expert knowledge to optimize the energy efficiency of a machine tool and is basically done by a decision tree which leads to a set of rules which will be explained in this article. This set of rules coordinate an optimization algorithm, which technically manipulates selected variables under the given rules. The development and is a result of a research which was done at the serial production of camshafts at the MB plant in Berlin.
  • Publication
    Energy Efficiency Optimization using AutomationML modeling and an EnPI methodology
    ( 2019)
    Thiele, Gregor
    ;
    Khorsandi, Niloufar
    ;
    Krüger, Jörg
    Industrial facilities are complex and heterogeneous systems in permanent technological change. The ambitions towards smart factories heighten the requirements for the flexible interconnection of various devices. These industrial entities are controlled, observed and optimized by many services. The tuning of process parameters of several linked components in order to boost the overall energy efficiency is one example of such services. AutomationML (AML) provides a hierarchical description language for industrial systems considering both structure and properties. An extension of the established standard allows for intuitive modeling of energy optimization problems. An approved energy performance indicator (EnPI) methodology was integrated in the libraries of AML in order to simplify and shorten the modeling procedure for the optimization task. The procedure is demonstrated using the example of an industrial cooling system.
  • Publication
    A practical approach to reduce energy consumption in a serial production environment by shutting down subsystems of a machine tool
    ( 2019)
    Can, Alperen
    ;
    Thiele, Gregor
    ;
    Krüger, Jörg
    ;
    Fisch, J.
    ;
    Klemm, C.
    Energy efficiency in production is becoming increasingly important for the automotive industry, motivated by political regulations and competitiveness. Many theoretical approaches to achieve an efficient production via advanced control have only been tested in experimental environments. Important for the transfer into serial production is the proof that all requirements (e.g. quantity and quality) will be met. For ensuring production on demand, machine tools (MT) imitate the real production process to keep themselves at operating temperature. All subsystems of a MT operate at full power in this state, disregarding its necessity. Shutting down these subsystems during non-productive periods is a promising approach for saving energy. This paper will present a method for shutting down components during non-productive periods, while ensuring the ability to produce on demand. Successful tests were already performed during live operation in a plant of a car manufacturer in Berlin, Germany.
  • Publication
    Quantification and compensation of systematic errors in pressure measurements applied to oil pipelines
    ( 2018)
    Thiele, Gregor
    ;
    Liu, Martin
    ;
    Chemnitz, Moritz
    ;
    Krüger, Jörg
    The monitoring of pipeline operation is an important research topic, especially for the detection and localization of leaks as well as for an efficient control. For these purposes, physical quantities in pipelines are calculated from measurement data on the basis of a mathematical model. In contrast to static models, adaptive models vary their parameters or even their structure to reach the most probable solution. But in most cases, even the best fit will hold residuals caused by discrepancies between the real system and its model. These residuals allow an estimation of travel-time delays of pressure waves and offsets in pressure values. The basic idea of our approach is to interpret these systematic, time-invariant errors of pressure measurements in pipelines either as sensor displacements or as technical defects. The proposed procedure leads to a hypothesis for a model update, regarding the sensor positions. This displacement compensation as well as a variance analysis was successfully applied to real data from a crude oil pipeline in Europe. A cross validation proves the general capability of the developed method to reduce the uncertainties.