基于预训练模型的中心分级燃烧室燃烧振荡预报方法

To promote the prediction of combustion oscillation in gas turbine combustors, a research approach combining pre-training and transfer learning is proposed. In the pre-training phase, contrastive learning using two types of flame images under short and long flame tubes is carried out to complete the self-supervised pre-training of the encoder. In the transfer phase, in addition to the direct transfer of constructing a linear classifier for feature encodings, Bayesian transfer learning with operating conditions parameters as prior conditions is proposed in this paper. The results show that the pre-trained model has a performance improvement of about 4.6% compared to traditional supervised learning models under two transfer learning methods. Meanwhile, transfer learning based on Bayesian inference exhibits better robustness compared to direct transfer. Through principal component analysis and hierarchical clustering, it is verified that the pre-trained model extracts more general thermoacoustic features from flame images.