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Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Anal

Description: Data-Driven Fault Detection for Industrial Processes by Zhiwen Chen Estimated delivery 3-12 business days Format Paperback Condition Brand New Description Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. Publisher Description Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed. Author Biography Zhiwen Chens research interests include multivariate statistical process monitoring, model-based and data-driven fault diagnosis as well as their application to industrial processes. He is currently working at the School of Information Science and Engineering at Central South University, China. Details ISBN 3658167556 ISBN-13 9783658167554 Title Data-Driven Fault Detection for Industrial Processes Author Zhiwen Chen Format Paperback Year 2017 Pages 112 Edition 1st Publisher Springer Fachmedien Wiesbaden GE_Item_ID:137559398; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys

Price: 101.24 USD

Location: Fairfield, Ohio

End Time: 2024-12-27T03:41:25.000Z

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Data-Driven Fault Detection for Industrial Processes: Canonical Correlation Anal

Item Specifics

Restocking Fee: No

Return shipping will be paid by: Buyer

All returns accepted: Returns Accepted

Item must be returned within: 30 Days

Refund will be given as: Money Back

ISBN-13: 9783658167554

Type: NA

Publication Name: NA

Book Title: Data-Driven Fault Detection for Industrial Processes : Canonical Correlation Analysis and Projection Based Methods

Number of Pages: Xix, 112 Pages

Language: English

Publisher: Springer Fachmedien Wiesbaden Gmbh

Publication Year: 2017

Topic: Engineering (General), Probability & Statistics / Multivariate Analysis, Electrical, Applied, Chemistry / Industrial & Technical

Illustrator: Yes

Genre: Mathematics, Technology & Engineering, Science

Item Weight: 63.7 Oz

Item Length: 8.3 in

Author: Zhiwen Chen

Item Width: 5.8 in

Format: Trade Paperback

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