Cross-Modal Virtual Sensing for Combustion Instability Monitoring

TL;DR

This paper introduces a cross-modal deep learning framework that reconstructs visual flame features from acoustic data to improve combustion instability detection, addressing hardware limitations in flame imaging.

Contribution

It presents a novel encoder-decoder architecture for cross-modal feature reconstruction, enhancing detection accuracy without requiring visual data in real-time systems.

Findings

Reconstruction of visual features from acoustic data is feasible.

Cross-modal features improve detection accuracy.

Framework applicable beyond combustion systems.

Abstract

In many cyber-physical systems, imaging can be an important but expensive or 'difficult to deploy' sensing modality. One such example is detecting combustion instability using flame images, where deep learning frameworks have demonstrated state-of-the-art performance. The proposed frameworks are also shown to be quite trustworthy such that domain experts can have sufficient confidence to use these models in real systems to prevent unwanted incidents. However, flame imaging is not a common sensing modality in engine combustors today. Therefore, the current roadblock exists on the hardware side regarding the acquisition and processing of high-volume flame images. On the other hand, the acoustic pressure time series is a more feasible modality for data collection in real combustors. To utilize acoustic time series as a sensing modality, we propose a novel cross-modal encoder-decoder architecture that can reconstruct cross-modal visual features from acoustic pressure time series in combustion systems. With the "distillation" of cross-modal features, the results demonstrate that the detection accuracy can be enhanced using the virtual visual sensing modality. By providing the benefit of cross-modal reconstruction, our framework can prove to be useful in different domains well beyond the power generation and transportation industries.