  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.
