Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods (Record no. 94806)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03697nam a22004695i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-1-4471-5185-2 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20140220082809.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130616s2013 xxk| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781447151852 |
| -- | 978-1-4471-5185-2 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-1-4471-5185-2 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q334-342 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TJ210.2-211.495 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UYQ |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TJFM1 |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM004000 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Aldrich, Chris. |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods |
| Medium | [electronic resource] / |
| Statement of responsibility, etc | by Chris Aldrich, Lidia Auret. |
| 264 #1 - | |
| -- | London : |
| -- | Springer London : |
| -- | Imprint: Springer, |
| -- | 2013. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XIX, 374 p. 208 illus., 151 illus. in color. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | Advances in Computer Vision and Pattern Recognition, |
| International Standard Serial Number | 2191-6586 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial Intelligence (incl. Robotics). |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Auret, Lidia. |
| Relator term | author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9781447151845 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Advances in Computer Vision and Pattern Recognition, |
| -- | 2191-6586 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-1-4471-5185-2 |
| 912 ## - | |
| -- | ZDB-2-SCS |
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