Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Record no. 51705)
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| 000 -LEADER | |
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| fixed length control field | 03717nam a22004815i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-1-4471-5185-2 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200420220218.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130616s2013 xxk| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781447151852 |
| -- | 978-1-4471-5185-2 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Aldrich, Chris. |
| 245 10 - TITLE STATEMENT | |
| Title | Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIX, 374 p. 208 illus., 151 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Advances in Computer Vision and Pattern Recognition, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Overview of Process Fault Diagnosis -- Artificial Neural Networks -- Statistical Learning Theory and Kernel-Based Methods -- Tree-Based Methods -- Fault Diagnosis in Steady State Process Systems -- Dynamic Process Monitoring -- Process Monitoring Using Multiscale Methods. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Auret, Lidia. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-1-4471-5185-2 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | London : |
| -- | Springer London : |
| -- | Imprint: Springer, |
| -- | 2013. |
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| -- | text |
| -- | txt |
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| -- | computer |
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| -- | rdamedia |
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| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2191-6586 |
| 912 ## - | |
| -- | ZDB-2-SCS |
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