Stochastic Modelling of the Flow-Front Evolution in a Vacuum Assisted Resin Transfer Moulding Process with Missing Data

TL;DR

This paper introduces a stochastic grey-box modeling approach using coupled SDEs and a modified Kalman filter to accurately track the flow-front in VARTM processes, even with missing sensor data and noise.

Contribution

It presents a novel coupled SDE-based grey-box model and a modified Kalman filter framework for flow-front tracking with missing data in VARTM.

Findings

Effective in handling missing sensor data

Robust against measurement noise

Performs well across various fault scenarios

Abstract

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.