Au S.-K. Operational Modal Analysis: Modeling, Bayesian Inference, Uncertainty Laws

Springer Nature Singapore Pte Ltd., 2017. — 552 p. — ISBN: 9811041172.This book presents operational modal analysis (OMA), employing a coherent and comprehensive Bayesian framework for modal identification and covering stochastic modeling, theoretical formulations, computational algorithms, and practical applications. Mathematical similarities and philosophical differences between Bayesian and classical statistical approaches to system identification are discussed, allowing their mathematical tools to be shared and their results correctly interpreted.
Many chapters can be used as lecture notes for the general topic they cover beyond the OMA context.
This book is primarily intended for graduate/senior undergraduate students and researchers, although practitioners will also find the book a useful reference guide. It covers materials from introductory to advanced level, which are classified accordingly to ensure easy access. Readers with an undergraduate-level background in probability and statistics will find the book an invaluable resource, regardless of whether they are Bayesian or non-Bayesian.ModelingSpectral Analysis of Deterministic Process
Structural Dynamics and Modal Testing
Spectral Analysis of Stationary Stochastic Process
Stochastic Structural Dynamics
Measurement Basics
Ambient Data Modeling and Analysis
Inference
Bayesian Inference
Classical Statistical Inference
Bayesian OMA Formulation
Bayesian OMA Computation
Algorithms
Single Mode Problem
Multi-mode Problem
Multi-setup Problem
Uncertainty Laws
Managing Identification Uncertainties
Theory of Uncertainty Laws
Appendixes
Complex Gaussian and Wishart Distribution
Hessian Under Constraints
Mathematical Tools