Items related to Unsupervised Process Monitoring and Fault Diagnosis...
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition) - Softcover
Book 31 of 86: Advances in Computer Vision and Pattern Recognition
Synopsis
This book describes the latest developments in nonlinear methods and their application in fault diagnosis. It details advances in machine learning theory and contains numerous case studies with real-world data from industry.
"synopsis" may belong to another edition of this title.
From the Back Cover
Algorithms for intelligent fault diagnosis of automated operations offer significant benefits to the manufacturing and process industries. Furthermore, machine learning methods enable such monitoring systems to handle nonlinearities and large volumes of data.
This unique text/reference describes in detail the latest advances in Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections.
Topics and features:
- Reviews the application of machine learning to process monitoring and fault diagnosis
- Discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods
- Examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning
- Describes the use of spectral methods in process fault diagnosis
This highly practical and clearly-structured work is an invaluable resource for all researchers and practitioners involved in process control, multivariate statistics and machine learning.
Dr. Chris Aldrich is a Professor in the Department of Metallurgical and Minerals Engineering at Curtin University, Perth, Australia. Dr. Lidia Auret is a Lecturer in the Department of Process Engineering at Stellenbosch University, South Africa.
"About this title" may belong to another edition of this title.
Search results for Unsupervised Process Monitoring and Fault Diagnosis...
Stock Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (eng)
Print on DemandSeller: Brook Bookstore On Demand, Napoli, NA, Italy
Seller rating 5 out of 5 stars
Condition: new. Questo è un articolo print on demand. Seller Inventory # 06044f0aa164de65be97184f8f6ef522
Buy New
£ 86.66
£ 35.97 shipping
Ships from Italy to U.S.A.
Ships from Italy to U.S.A.
Quantity: Over 20 available
Seller Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Published by
Springer London Aug 2016, 2016
ISBN 10: 1447171608
ISBN 13: 9781447171607
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis. 396 pp. Englisch. Seller Inventory # 9781447171607
Seller Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Print on DemandSeller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Describes the latest developments in nonlinear methods and their application in fault diagnosisDiscusses in detail several advances in machine learning theoryContains numerous case studies with real-world data from industryThis uniq. Seller Inventory # 458490319
Buy New
£ 93.01
£ 41.95 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: Over 20 available
Stock Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. pp. 396. Seller Inventory # 26375557139
Seller Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Seller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods | Chris Aldrich (u. a.) | Taschenbuch | Advances in Computer Vision and Pattern Recognition | xix | Englisch | 2016 | Springer | EAN 9781447171607 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 103477878
Stock Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)
Print on DemandSeller: Majestic Books, Hounslow, United Kingdom
Seller rating 4 out of 5 stars
Condition: New. Print on Demand pp. 396. Seller Inventory # 370488268
Buy New
£ 148.92
£ 6.50 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 4 available
Seller Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis. Seller Inventory # 9781447171607
Seller Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Published by
Springer, Springer Aug 2016, 2016
ISBN 10: 1447171608
ISBN 13: 9781447171607
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 396 pp. Englisch. Seller Inventory # 9781447171607
Stock Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)
Print on DemandSeller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 4 out of 5 stars
Condition: New. PRINT ON DEMAND pp. 396. Seller Inventory # 18375557145
Stock Image
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Advances in Computer Vision and Pattern Recognition)
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: Brand New. reprint edition. 393 pages. 9.25x6.10x0.93 inches. In Stock. Seller Inventory # zk1447171608
Buy New
£ 162.65
£ 12.50 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available