Combined use of partial least squares regression and neural network for diagnosis tasks

This work deals with a diagnosis system, based on a combined use of partial least squares regression (PLS) and neural network (NN). An application concerning the French railway track/vehicle transmission system illustrates this approach. It is shown that a reliable selection of a reduced set of relevant descriptors is made by the PLS regression. Moreover, the projection of the data on the first PLS plane allows to highlight trajectories of the evolution of the system state between different classes. The modeling of the process state is performed by a multilayer NN. In this case, the PLS algorithm provides also a suitable approach to initialize the NN weights and to determine the optimal number of hidden nodes.