Tdnn-Based Engine In-Cylinder Pressure Estimation from Shaft Velocity Spectral Representation - Andrés F. Valencia-Duque; David A. Cárdenas-Peña; Andrés M. Álvarez-Meza; Álvaro A. Orozco-Gutiérrez; Héctor F. Quintero-Riaza (Sensors, 2021)
Pressure is one of the essential variables to give information about engine condition and monitoring. Direct recording of this signal is complex and invasive, while angular velocity can be measured. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. In this paper, a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter, is proposed to estimate the in-cylinder pressure of a single-cylinder internal combustion engine (ICE) from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN’s delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2 <semantics>>0.9</semantics>, avoiding complicated pre-processing steps.