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.
翻译:在许多网络物理系统中,成像可能是一个重要但昂贵或“难以部署”的感测模式。其中一个例子就是利用火焰图像探测燃烧不稳定性,深层学习框架已经展示出最先进的性能。还表明,拟议的框架相当可靠,因此,域专家能够有足够的信心在实际系统中使用这些模型来防止意外事件。然而,火焰成像在发动机燃烧器中并不是一种常见的感测模式。因此,目前硬件方面在获取和处理高容量火焰图像方面存在障碍。另一方面,声压时间序列是真实燃烧器中数据收集的一种更可行的模式。为了将声学时间序列用作一种感测模式,我们提出了一个新型的跨模式编码解码器结构,可以重建燃烧系统中的声压时间序列中的跨式视觉特征。随着跨模式特征的“蒸馏”,结果表明,使用虚拟视觉感测模式可以提高探测的准确性。通过提供跨模式重建的好处,我们的框架可以在发电和发电行业以外的不同领域发挥作用。