Assessment of flame stability through a convolutional denoising autoencoder and statistical analysis

Abstract

Flame stability assessment is essential for optimizing combustion operation and improving combustion quality. However, an accurate and reliable assessment of stability is difficult, heavily relying on prior expert knowledge and massive labeled data. This study proposes a novel method for flame stability assessment through flame images and deep learning techniques. In this method, the deep image features are extracted by an unsupervised convolutional denoising autoencoder (CDAE), and then quantitatively analyzed by a stability index. In particular, the CDAE introduces a new loss function composed of denoising coding constraints and reconstruction similarity to improve its training efficiency. The stability index is established based on clustering analysis and statistical analysis of the deep image features, with a numerical interval of [0, 1]. The effectiveness of the proposed method is verified by the flame images obtained from ethylene-air diffusion combustion conditions. Results show that the proposed method extracts representative flame features accurately and quantifies the flame stability with strong robustness and generalization ability.