Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

Aldrich, Chris
Auret, Lidia

Hardcover

Series: Advances in Computer Vision and Pattern Recognition

General Computers

ISBN10: 1447151844
ISBN13: 9781447151845
Publisher: Springer Pg
Published: Jul 9 2013
Pages: 374
Weight: 2.05
Height: 1.00 Width: 6.20 Depth: 9.20
Language: English
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.

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