Neural networks and statistical decision making for fault diagnosis in energy conversion systems

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 χ2 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.