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In many cyber-physical systems, imaging can be an important but expensive or'difficult to deploy' sensing modality. One such example is detectingcombustion instability using flame images, where deep learning frameworks havedemonstrated state-of-the-art performance. The proposed frameworks are alsoshown to be quite trustworthy such that domain experts can have sufficientconfidence to use these models in real systems to prevent unwanted incidents.However, flame imaging is not a common sensing modality in engine combustorstoday. Therefore, the current roadblock exists on the hardware side regardingthe acquisition and processing of high-volume flame images. On the other hand,the acoustic pressure time series is a more feasible modality for datacollection in real combustors. To utilize acoustic time series as a sensingmodality, we propose a novel cross-modal encoder-decoder architecture that canreconstruct cross-modal visual features from acoustic pressure time series incombustion systems. With the "distillation" of cross-modal features, theresults demonstrate that the detection accuracy can be enhanced using thevirtual visual sensing modality. By providing the benefit of cross-modalreconstruction, our framework can prove to be useful in different domains wellbeyond the power generation and transportation industries.

Cross-Modal Virtual Sensing for Combustion Instability Monitoring