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Investigation of Fault Detection Techniques for an Industrial Pneumatic Actuator Using Neural Network: DAMADICS Case Study

  • V. Elakkiya
  • K. Ram Kumar
  • V. Gomathi
  • S. Rakesh Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)

Abstract

The objective of this work was to develop the novel approach for fault detection using neural network in industrial actuator for DAMADICS benchmark case. This neural network model has the ability to produce effective result for fault detection. In this paper, a model-based technique is proposed for the residual generation which results from the deviation of fault-free behavior of actuator from faulty behavior on actuator. The actuator is the multi-input–multi-output (MIMO) system which is designed using four kinds of neural network architectures (NNARX, NNARMAX, NNRARX, and feed forward), and best structure is chosen based on performance indices. The actuator faults can be grouped using k-means clustering technique. This technique is applied to the DAMADICS benchmark case.

Keywords

Fault detection Neural network k-means DAMADICS 

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Copyright information

© Springer India 2015

Authors and Affiliations

  • V. Elakkiya
    • 1
  • K. Ram Kumar
    • 1
  • V. Gomathi
    • 1
  • S. Rakesh Kumar
    • 1
  1. 1.Department of Electronics and Instrumentation EngineeringSASTRA UniversityThanjavurIndia

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