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Operational Modal Analysis - Modeling, Bayesian Inference, Uncertainty Laws (Hardcover, 1st ed. 2017)
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Operational Modal Analysis - Modeling, Bayesian Inference, Uncertainty Laws (Hardcover, 1st ed. 2017)
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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/7EVTXG Many 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.
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