  This paper presents a physics-informed neural network (PINN) approach for
monitoring the health of diesel engines. The aim is to evaluate the engine
dynamics, identify unknown parameters in a "mean value" model, and anticipate
maintenance requirements. The PINN model is applied to diesel engines with a
variable-geometry turbocharger and exhaust gas recirculation, using measurement
data of selected state variables. The results demonstrate the ability of the
PINN model to predict simultaneously both unknown parameters and dynamics
accurately with both clean and noisy data, and the importance of the
self-adaptive weight in the loss function for faster convergence. The input
data for these simulations are derived from actual engine running conditions,
while the outputs are simulated data, making this a practical case study of
PINN's ability to predict real-world dynamical systems. The mean value model of
the diesel engine incorporates empirical formulae to represent certain states,
but these formulae may not be generalizable to other engines. To address this,
the study considers the use of deep neural networks (DNNs) in addition to the
PINN model. The DNNs are trained using laboratory test data and are used to
model the engine-specific empirical formulae in the mean value model, allowing
for a more flexible and adaptive representation of the engine's states. In
other words, the mean value model uses both the PINN model and the DNNs to
represent the engine's states, with the PINN providing a physics-based
understanding of the engine's overall dynamics and the DNNs offering a more
engine-specific and adaptive representation of the empirical formulae. By
combining these two approaches, the study aims to offer a comprehensive and
versatile approach to monitoring the health and performance of diesel engines.
