The development and application of multivariate statistical techniques
in process monitoring has gained substantial interest over the past two
decades in academia and industry alike. Initially developed for
monitoring and fault diagnosis in complex systems, such techniques have
been refined and applied in various engineering areas, for example
mechanical and manufacturing, chemical, electrical and electronic, and
power engineering. The recipe for the tremendous interest in
multivariate statistical techniques lies in its simplicity and
adaptability for developing monitoring applications. In contrast,
competitive model, signal or knowledge based techniques showed their
potential only whenever cost-benefit economics have justified the
required effort in developing applications.
Statistical Monitoring of Complex Multivariate Processes presents
recent advances in statistics based process monitoring, explaining how
these processes can now be used in areas such as mechanical and
manufacturing engineering for example, in addition to the traditional
chemical industry.
This book:
- Contains a detailed theoretical background of the component
technology.
- Brings together a large body of work to address the field's drawbacks,
and develops methods for their improvement.
- Details cross-disciplinary utilization, exemplified by examples in
chemical, mechanical and manufacturing engineering.
- Presents real life industrial applications, outlining deficiencies in
the methodology and how to address them.
- Includes numerous examples, tutorial questions and homework
assignments in the form of individual and team-based projects, to
enhance the learning experience.
- Features a supplementary website including Matlab algorithms and data
sets.
This book provides a timely reference text to the rapidly evolving area
of multivariate statistical analysis for academics, advanced level
students, and practitioners alike.
