Comparer des méthodes

	Fusion de capteurs	Surveillance de l'intégrité des structures
Domaine	Fusion de données	Génie civil
Famille	Process / pipeline	Process / pipeline
Année d'origine	2013	1980s–1990s (formalized as a discipline ~1993–2001)
Auteur d'origine	Khaleghi, Khamis, Karray & Razavi	Multiple contributors (Charles Farrar, Keith Worden, and the broader SHM research community)
Type	Multi-source information integration pipeline	Engineering monitoring and diagnostic framework
Source fondatrice	Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44.	Farrar, C. R., & Worden, K. (2007). An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A, 365(1851), 303–315.
Alias	Multisensor Data Fusion, Multi-Sensor Integration, Information Fusion, Sensör Füzyonu	SHM, damage detection monitoring, condition monitoring of structures, vibration-based structural monitoring
Apparentées	3	3
Résumé	Sensor fusion is a computational process that combines data from multiple heterogeneous sensors to produce an estimate of the environment that is more accurate, complete, and reliable than any single source alone. Systematized as a formal field by Khaleghi, Khamis, Karray, and Razavi in their 2013 state-of-the-art review in Information Fusion, the discipline addresses imperfections such as noise, incompleteness, temporal misalignment, and conflicting readings that arise whenever multiple sensing modalities operate in parallel.	Structural Health Monitoring (SHM) is a process-based engineering methodology used in civil, mechanical, and aerospace engineering to continuously assess the condition of structures — bridges, buildings, dams, pipelines, and aircraft — through embedded or attached sensor networks. By acquiring real-time or periodic measurement data and applying signal processing and statistical pattern recognition, SHM aims to detect, locate, classify, and quantify damage before it reaches a critical state, enabling evidence-based maintenance decisions.