As the complexity of engineering systems is constantly increasing, their
design calls for the sustainability and reliability of their high
performance. Fault diagnosis (FD) is a vital system tool that addresses
this critically-important issue. This book presents a clear and easy
introduction to the sequential integration of the two major approaches
to FD with application, namely model-free and model-based and draws on
the power of the two approaches vitally for the detection of incipient
faults which are hard to uncover by a single approach alone. In the
model-free approach, this book explores from the intuitive approach of
plausibility checks to the more sophisticated learning approaches (ANN,
FL, Genetic algorithms, etc.) and their hybrid versions. In the
model-based approach, the book presents relies on the powerful and
ubiquitous Kalman filter and presents new material on: - A new
model-order selection criterion that is appealing to practicing
engineers. - A new and fresh look at the design of Kalman filters based
on the simple and powerful internal model principle. As such, this book
will be appealing to both researchers and practitioners alike.