Abstract
Abstract The reinforcement learning is a well-known approach for solving optimization problems having limited information about plant dynamics. Its key element, named “critic” is aimed at prediction of future “punish/reward” signals received as a result of undertaken control actions. The main idea in the present work is to use such a “critic” element for prediction of approaching alarm situations based on limited measurement information from the industrial plant. In order to train the critic network in real time it is proposed to use a special kind of a fast trainable recurrent neural network, called Echo State Network (ESN). The approach proposed is demonstrated on an example for predictive maintenance of a mill fan in Maritsa East 2 Thermal Power Plant
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