The role of data fusion in predictive maintenance using digital twin

TLDR

Modern aerospace industry is shifting from reactive to predictive maintenance to boost availability, extend life cycles, and cut costs, with digital twins—living models that combine multiphysics simulation and data analytics—continuously adapting and forecasting asset behavior. The paper reviews and identifies how data fusion within a digital twin framework, integrated with IoT, advances aerospace platform autonomy for predictive maintenance. Data fusion, including sensor‑to‑sensor, sensor‑to‑model, and model‑to‑model techniques, drives the flow of information from raw data to high‑level decision making in the digital twin framework.

Abstract

Modern aerospace industry is migrating from reactive to proactive and predictive maintenance to increase platform operational availability and efficiency, extend its useful life cycle and reduce its life cycle cost. Multiphysics modeling together with data-driven analytics generate a new paradigm called “Digital Twin.” The digital twin is actually a living model of the physical asset or system, which continually adapts to operational changes based on the collected online data and information, and can forecast the future of the corresponding physical counterpart. This paper reviews the overall framework to develop a digital twin coupled with the industrial Internet of Things technology to advance aerospace platforms autonomy. Data fusion techniques particularly play a significant role in the digital twin framework. The flow of information from raw data to high-level decision making is propelled by sensor-to-sensor, sensor-to-model, and model-to-model fusion. This paper further discusses and identifies the role of data fusion in the digital twin framework for aircraft predictive maintenance.