方法对比

	三角测量传感器数据采集	传感器融合	结构健康监测
领域	调查方法论	数据融合	土木工程
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1980s–1990s (formalized in sensor fusion and IoT research)	2013	1980s–1990s (formalized as a discipline ~1993–2001)
提出者	Hall & Llinas and the multisensor data fusion community	Khaleghi, Khamis, Karray & Razavi	Multiple contributors (Charles Farrar, Keith Worden, and the broader SHM research community)
类型	Quantitative data collection technique	Multi-source information integration pipeline	Engineering monitoring and diagnostic framework
开创性文献	Hall, D. L., & Llinas, J. (Eds.). (1997). Handbook of Multisensor Data Fusion. CRC Press. ISBN: 978-0849323798	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.
别名	multi-sensor triangulation, sensor fusion data collection, redundant sensor sampling, cross-sensor validation	Multisensor Data Fusion, Multi-Sensor Integration, Information Fusion, Sensör Füzyonu	SHM, damage detection monitoring, condition monitoring of structures, vibration-based structural monitoring
相关	2	3	3
摘要	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.	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.