In-cylinder pressure reconstruction from engine block vibrations via a branched convolutional neural network

We introduce a novel approach to reconstructing the in-cylinder pressure trace from vibration signals recorded with common knock sensors. The proposed methodology is purely data-driven and employs a convolutional neural network that has two distinct branches. Each branch is allowed to learn individual aspects of the mapping process, with boundary conditions within the model architecture set to incentivize the individual branches to learn low-frequency and high-frequency contents of the pressure trace.