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. The authors provide their data freely in the web at https: doi.org
10.7910 DVN 7EVTXGMany chapters can be used as lecture notes for the general topic they cover
beyond the OMA context. After an introductory chapter (1) Chapters 2¿7 present the general
theory of stochastic modeling and analysis of ambient vibrations. Readers are first introduced
to the spectral analysis of deterministic time series (2) and structural dynamics (3) which do
not require the use of probability concepts. The concepts and techniques in these chapters are
subsequently extended to a probabilistic context in Chapter 4 (on stochastic processes) and in
Chapter 5 (on stochastic structural dynamics). In turn Chapter 6 introduces the basics of
ambient vibration instrumentation and data characteristics while Chapter 7 discusses the
analysis and simulation of OMA data covering different types of data encountered in practice.
Bayesian and classical statistical approaches to system identification are introduced in a
general context in Chapters 8 and 9 respectively. Chapter 10 provides an overview of different
Bayesian OMA formulations followed by a general discussion of computational issues in Chapter
11. Efficient algorithms for different contexts are discussed in Chapters 12¿14 (single mode
multi-mode and multi-setup). Intended for readers with a minimal background in mathematics
Chapter 15 presents the ¿uncertainty laws¿ in OMA one of the latest advances that establish
the achievable precision limit of OMA and provide a scientific basis for planning ambient
vibration tests. Lastly Chapter 16 discusses the mathematical theory behind the results in
Chapter 15 addressing the needs of researchers interested in learning the techniques for
further development. Three appendix chapters round out the coverage.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.
