Molina López, José ManuelIsasi, PedroBerlanga de Jesús, AntonioSanchis de Miguel, María Araceli2009-04-142009-04-142000-06Engineering Applications of Artificial Intelligence, 13, 3 (2000), 357-3690952-1976https://hdl.handle.net/10016/3939The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.application/pdfeng© ElsevierPredictive maintenanceNeural networksARTLVQPower plantsExpert systemsresearch articleInformática10.1016/S0952-1976(00)00009-9open access3573369Engineering Applications of Artificial Intelligence13