A neural network model is presented for monitoring events coded in image sequences. The image sequences define the sampling of high frequency space/time changeable phenomena where both the morphological and densitometric aspects of the scene are taken into account. In this frame, a model for implementing a multilevel neural network architecture is proposed. This model is tested in the field of power production for monitoring the combustion instability degree in power plant gas combustors. The main goal of this study is to provide a support for preventing the oscillation states in the combustor's flame front and to characterize the instability itself. The work was developed in collaboration with ENEL Production and Research S.p.A. (Italian National Department for Electric Power), which supplied the study cases and the technical support for conducting the experiments. Preliminary results show the effectiveness of the proposed approach.
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机译：提出了用于监视以图像序列编码的事件的神经网络模型。图像序列定义了高频空间/时间可变现象的采样，其中考虑了场景的形态学和光密度学方面。在此框架中，提出了一种用于实现多层神经网络体系结构的模型。该模型在发电领域进行了测试，以监测电厂燃气燃烧器的燃烧不稳定性程度。这项研究的主要目的是为防止燃烧器火焰前沿的振荡状态提供支持，并描述不稳定性本身。该工作是与ENEL Production and Research S.p.A.（意大利国家电力局）合作开发的，该公司提供了研究案例和进行实验的技术支持。初步结果表明了该方法的有效性。
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