The availability of advanced information technologies such as the
various types of automatic data acquisitions and sensor systems has
created a tremendous capability to collect valuable process data.
The effective and timely processing of this data is the backbone of
intelligent process monitoring and control. The need for more
practical process monitoring models continues to grow as these
technologies become more sophisticated. This book presents
effective methods for processing sensor data in order to achieve
intelligent process monitoring and control strategies. In
developing these methods, the following issues were considered: (a)
how to represent secondary data in such a way that the features
conserve the condition information essential for real-time
decisions; (b) how to obtain a set of parsimonious features that
are able to capture new information and also preserve the condition
information in the original data; and (c) how to reduce the
uncertainty in selecting subset features for prediction purposes.
The book is an indispensable resource for researchers,
professionals, and graduate students in industrial engineering,
manufacturing, and related fields of study.
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