Combustion Condition Monitoring Through Deep Learning Networks

Combustion condition monitoring is essential in a power plant for maintaining stable operations and operational safety. Therefore it is crucial to develop an intelligent combustion monitoring system. Existing traditional methods not only need a large quantity of labeled data but also require rebuilding monitoring model for new conditions. Aiming these problems, the present study proposes a novel approach combining denoising auto-encoder (DAE) and generative adversarial network (GAN) to monitor combustion condition. By using the learning mechanism of the GAN, the robust feature extraction ability of DAE as a generator is improved. These features are then fed into the Gaussian process classifier (GPC) for condition identification. Especially, newly occurring conditions can be correctly classified by simply training the GPC, rather than training from scratch. Experiments performed on a gaseous combustor indicate that the proposed approach can extract representative features accurately and achieve high performance in combustion condition monitoring with the accuracy of 98.5% for original conditions and 97.8% for the new conditions.