  The real-time fault monitoring and control of the Vacuum Assisted Resin
Transfer Moulding (VARTM) production process requires a knowledge of the
position of the epoxy flow-front inside the mould. Therefore, a fast and
accurate flow-front tracking system capable of combining the underlying physics
of the flow-front dynamics with the measured data is highly prized. Stochastic
differential equations (SDEs) based grey-box models deliver a good trade-off
between high fidelity models and data-driven black-box models for designing
such a flow-front position tracking system. In this paper, we propose a simple
yet novel coupled SDE based spatiotemporal grey-box model of the flow-front
dynamics in case of missing sensor information. The proposed method uses the
finite difference approximation of the spatial domain of the flow-front for
estimating spatial flow pattern of the epoxy. Furthermore, to accommodate for
the missing sensor data, we utilise a modified version of the
continuous-discrete extended Kalman filter (CD-EKF) based estimation framework
for SDEs that takes into consideration the effective dimension of the
measurement space during the identification process. The performance of the
method is evaluated for various common sensor faults scenarios at different
levels of measurement noise and sampling rates.
