Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks

  • Cláudio Rebelo de SáEmail author
  • Arvind Kumar Shekar
  • Hugo Ferreira
  • Carlos Soares
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)


Sensors are susceptible to failure when exposed to extreme conditions over long periods of time. Besides they can be affected by noise or electrical interference. Models (Machine Learning or others) obtained from these faulty and noisy sensors may be less reliable. In this paper, we propose a data augmentation approach for making neural networks more robust to missing and faulty sensor data. This approach is shown to be effective in a real life industrial application that uses data of various sensors to predict the wear of an automotive fuel-system component. Empirical results show that the proposed approach leads to more robust neural network in this particular application than existing methods.



We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Cláudio Rebelo de Sá
    • 1
    Email author
  • Arvind Kumar Shekar
    • 2
  • Hugo Ferreira
    • 3
  • Carlos Soares
    • 4
  1. 1.Twente UniversityEnschedeNetherlands
  2. 2.Robert Bosch GmbHStuttgartGermany
  3. 3.INESC TECPortoPortugal
  4. 4.Faculdade de EngenhariaUniversidade do PortoPortoPortugal

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