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Neural Networks and Statistical Decision Making for Fault Diagnosis in Energy Conversion Systems

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Artificial Intelligence Techniques for a Scalable Energy Transition

Abstract

The chapter proposes neural networks and statistical decision making for fault diagnosis in energy conversion systems. It considers the condition monitoring problem for an energy conversion system comprising a solar power unit, a DC-DC converter, and a DC motor. The dynamic model of this energy conversion system is taken to be unknown and is reconstructed from its input and output measurements, being accumulated at different operating conditions, and taking finally the form of a neural network. Actually, the neural model consists of a hidden layer of Gauss–Hermite polynomial activation functions and an output layer of linear weights. The neural network is trained with the use of first-order gradient algorithms and the resulting model is taken to represent the fault-free functioning of the energy conversion system. To conclude about the existence of a fault, the measurements of the real output of the energy conversion system are compared against the estimated outputs which are provided by the neural model. Thus, the residuals’ sequence is generated. It is shown that the sum of the squares of the residuals’ vectors, multiplied with the inverse of the associated covariance matrix, stands for a stochastic variable (statistical test) which follows the χ 2 distribution. By selecting the 96% or the 98% confidence intervals of this distribution one can have a precise and almost infallible decision making tool about the appearance of faults in the energy conversion system.

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Author information

Authors and Affiliations

  1. Unit of Industrial Automation, Industrial Systems Institute, Rion Patras, Greece

    Gerasimos Rigatos

  2. Department of Electrical and Computer Engineering, University of Patras, Rion Patras, Greece

    Dimitrios Serpanos & Vasilios Siadimas

  3. Department of Management and Innovations Systems, University of Salerno, Fisciano, Italy

    Pierluigi Siano

  4. GE Global Research, General Electric Company, Niskayuna, NY, USA

    Masoud Abbaszadeh

Authors
  1. Gerasimos Rigatos
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  2. Dimitrios Serpanos
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  3. Vasilios Siadimas
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  4. Pierluigi Siano
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  5. Masoud Abbaszadeh
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Corresponding author

Correspondence to Gerasimos Rigatos .

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Editors and Affiliations

  1. Institute Mines-Telecom Lille Douai, Douai, France

    Moamar Sayed-Mouchaweh

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Rigatos, G., Serpanos, D., Siadimas, V., Siano, P., Abbaszadeh, M. (2020). Neural Networks and Statistical Decision Making for Fault Diagnosis in Energy Conversion Systems. In: Sayed-Mouchaweh, M. (eds) Artificial Intelligence Techniques for a Scalable Energy Transition. Springer, Cham. https://doi.org/10.1007/978-3-030-42726-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-42726-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42725-2

  • Online ISBN: 978-3-030-42726-9

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