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.
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.
€71.99
O autorze
Uwe Kruger, The Petroleum Institute, Abu Dhabi, United Arab EmiratesLei Xie, Institute of Cyber-Systems & Control, Zhejiang University, China
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Język Angielski ● Format PDF ● Strony 472 ● ISBN 9781118381267 ● Rozmiar pliku 11.5 MB ● Wydawca John Wiley & Sons ● Opublikowany 2012 ● Ydanie 1 ● Do pobrania 24 miesięcy ● Waluta EUR ● ID 2503764 ● Ochrona przed kopiowaniem Adobe DRM
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