Comparer des méthodes

	Collecte de données par capteurs triangulés	Surveillance de l'intégrité des structures
Domaine	Méthodologie d'enquête	Génie civil
Famille	Process / pipeline	Process / pipeline
Année d'origine	1980s–1990s (formalized in sensor fusion and IoT research)	1980s–1990s (formalized as a discipline ~1993–2001)
Auteur d'origine	Hall & Llinas and the multisensor data fusion community	Multiple contributors (Charles Farrar, Keith Worden, and the broader SHM research community)
Type	Quantitative data collection technique	Engineering monitoring and diagnostic framework
Source fondatrice	Hall, D. L., & Llinas, J. (Eds.). (1997). Handbook of Multisensor Data Fusion. CRC Press. ISBN: 978-0849323798	Farrar, C. R., & Worden, K. (2007). An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A, 365(1851), 303–315.
Alias	multi-sensor triangulation, sensor fusion data collection, redundant sensor sampling, cross-sensor validation	SHM, damage detection monitoring, condition monitoring of structures, vibration-based structural monitoring
Apparentées	2	3
Résumé	Triangulated sensor data collection deploys two or more independent sensors measuring the same phenomenon simultaneously, then cross-validates and aggregates their readings to obtain data that is more accurate, robust, and trustworthy than any single sensor alone. Widely used in environmental monitoring, structural health monitoring, IoT systems, and field experiments, the approach borrows the logic of triangulation from research methodology — using multiple independent sources to converge on a more reliable measurement.	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.