方法对比

	结构健康监测	有限元分析	统计可靠性分析
领域	土木工程	材料科学	可靠性
方法族	Process / pipeline	Process / pipeline	Regression model
起源年份	1980s–1990s (formalized as a discipline ~1993–2001)	1943	1998
提出者	Multiple contributors (Charles Farrar, Keith Worden, and the broader SHM research community)	Richard Courant	William Meeker & Luis Escobar
类型	Engineering monitoring and diagnostic framework	Computational method	Parametric lifetime modeling
开创性文献	Farrar, C. R., & Worden, K. (2007). An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A, 365(1851), 303–315.	Zienkiewicz, O. C., & Taylor, R. L. (1977). The Finite Element Method in Engineering Science. McGraw-Hill.	Meeker, W. Q., & Escobar, L. A. (1998). Statistical Methods for Reliability Data. Wiley. ISBN: 978-0-471-14328-4
别名	SHM, damage detection monitoring, condition monitoring of structures, vibration-based structural monitoring	FEA, finite element method	Life Data Analysis, Survival Analysis (Engineering), Time-to-Failure Analysis, Güvenilirlik Analizi
相关	3	4	3
摘要	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.	Finite Element Analysis (FEA) is a numerical technique for obtaining approximate solutions to boundary value problems described by differential equations. Developed systematically by Richard Courant in 1943 and popularized by Clough in the 1960s, FEA divides a complex domain into smaller, simpler elements to solve engineering problems involving stress, strain, heat transfer, and fluid flow. It is the dominant computational method in materials science for predicting material behavior under various loading conditions.	Statistical reliability analysis models the time-to-failure of components, systems, or products using parametric lifetime distributions fitted to observed or censored failure data. Formalized comprehensively by William Q. Meeker and Luis A. Escobar in their 1998 Wiley monograph, the framework integrates maximum likelihood estimation, censoring mechanisms, and distributional diagnostics to produce probability-of-failure curves, hazard rates, and quantile estimates that support design, warranty, and maintenance decisions.