Skip to content

Booksellers & Trade Customers: Sign up for online bulk buying at trade.atlanticbooks.com for wholesale discounts

Booksellers: Create Account on our B2B Portal for wholesale discounts

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

by Chris Aldrich
Save 35% Save 35%
Current price ₹11,752.00
Original price ₹18,079.00
Original price ₹18,079.00
Original price ₹18,079.00
(-35%)
₹11,752.00
Current price ₹11,752.00

Imported Edition - Ships in 12-14 Days

Free Shipping in India on orders above Rs. 500

Request Bulk Quantity Quote
+91
Book cover type: Hardcover
  • ISBN13: 9781447151845
  • Binding: Hardcover
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 374
  • Original Price: EUR 159.99
  • Language: English
  • Edition: 2013
  • Item Weight: 930 grams
  • BISAC Subject(s): Artificial Intelligence / General

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.

Trusted for over 49 years

Family Owned Company

Secure Payment

All Major Credit Cards/Debit Cards/UPI & More Accepted

New & Authentic Products

India's Largest Distributor

Need Support?

Whatsapp Us