Data fusion approaches for structural health monitoring and system identification: Past, present, and future

TLDR

Structural health monitoring has evolved over decades, with recent sensor and data‑acquisition advances creating a Big Data era that offers abundant heterogeneous data but poses challenges for aggregating multi‑sensor information into robust assessments. The article reviews recent data fusion applications in structural health monitoring, discusses challenges, and proposes a roadmap for future research. It surveys state‑of‑the‑art theoretical concepts and applications of data fusion for structural health monitoring. The review identifies key challenges in data fusion for structural health monitoring and outlines a roadmap for future research.

Abstract

During the past decades, significant efforts have been dedicated to develop reliable methods in structural health monitoring. The health assessment for the target structure of interest is achieved through the interpretation of collected data. At the beginning of the 21st century, the rapid advances in sensor technologies and data acquisition platforms have led to the new era of Big Data, where a huge amount of heterogeneous data are collected by a variety of sensors. The increasing accessibility and diversity of the data resources provide new opportunities for structural health monitoring, while the aggregation of information obtained from multiple sensors to make robust decisions remains a challenging problem. This article presents a comprehensive review of the recent data fusion applications in structural health monitoring. State-of-the-art theoretical concepts and applications of data fusion in structural health monitoring are presented. Challenges for data fusion in structural health monitoring are discussed, and a roadmap is provided for future research in this area.