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Model-Based Error Detection for Industrial Automation Systems Using LSTM Networks

  • Sheng DingEmail author
  • Andrey Morozov
  • Silvia Vock
  • Michael Weyrich
  • Klaus Janschek
Conference paper
  • 183 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12297)

Abstract

The increasing complexity of modern automation systems leads to inevitable faults. At the same time, structural variability and untrivial interaction of the sophisticated components makes it harder and harder to apply traditional fault detection methods. Consequently, the popularity of Deep Learning (DL) fault detection methods grows. Model-based system design tools such as Simulink allow the development of executable system models. Besides the design flexibility, these models can provide the training data for DL-based error detectors.

This paper describes the application of an LSTM-based error detector for a system of two industrial robotic manipulators. A detailed Simulink model provides the training data for an LSTM predictor. Error detection is achieved via intelligent processing of the residual between the original signal and the LSTM prediction using two methods. The first method is based on the non-parametric dynamic thresholding. The second method exploits the Gaussian distribution of the residual. The paper presents the results of extensive model-based fault injection experiments that allow the comparison of these methods and the evaluation of the error detection performance for varying error magnitude.

Keywords

Error detection Simulink Deep learning LSTM Time-series data Industrial robots 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sheng Ding
    • 1
    Email author
  • Andrey Morozov
    • 2
  • Silvia Vock
    • 3
  • Michael Weyrich
    • 2
  • Klaus Janschek
    • 1
  1. 1.Institute of AutomationTechnische Universität DresdenDresdenGermany
  2. 2.Institute of Industrial Automation and Software EngineeringUniversity of StuttgartStuttgartGermany
  3. 3.Bundesanstalt für Arbeitsschutz und ArbeitsmedizinDresdenGermany

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