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Data-driven fault diagnosis for complex industrial processestowards fault prediction, detection and identification /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven fault diagnosis for complex industrial processesby Hongpeng Yin ... [et al.].
Reminder of title:	towards fault prediction, detection and identification /
other author:	Yin, Hongpeng.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xiv, 208 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Industrial engineering.
Online resource:	https://doi.org/10.1007/978-981-96-3153-7
ISBN:	9789819631537$q(electronic bk.)

Data-driven fault diagnosis for complex industrial processestowards fault prediction, detection and identification /
Data-driven fault diagnosis for complex industrial processestowards fault prediction, detection and identification /[electronic resource] :by Hongpeng Yin ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xiv, 208 p. :ill., digital ;24 cm. - Engineering applications of computational methods,v. 222662-3374 ;. - Engineering applications of computational methods ;v.2..

Introduction -- Update strategy based Gaussian processes regression for processes fault prediction with incremental data -- Relevant variable selection and SVDD for processes fault detection with incremental redundant data -- Industrial process monitoring using fisher discriminant based global local preserving projection.

This book summarizes techniques of fault prediction, detection, and identification, all included specifically in the data-driven fault diagnosis requirements within industrial processes, drawing from the combination of data science, machine learning, and domain-specific expertise. In the modern industrial processes, where efficiency, productivity, and safety stand as paramount pillars, the pursuit of fault diagnosis has become more crucial than ever. The widespread use of computer systems, along with new sensor hardware, generates significant quantities of real-time process data. It has been frequently asked what could be done with both the real-time and archived historical data, to not only promising efficiency but providing prospect of a brighter, more resilient future. This book starts with the definition, related work, and open test-bed for industrial process fault diagnosis. Then, it presents several data-driven methods on fault prediction (Part I), fault detection (Part II), and fault diagnosis (Part III), with consideration of properties of industrial processes, such as varying operation modes, non-Gaussian, nonlinearity. It distills cutting-edge methodologies and insights which may inspire for industrial practitioners, researchers, and academicians alike.

ISBN: 9789819631537$q(electronic bk.)

Standard No.: 10.1007/978-981-96-3153-7doiSubjects--Topical Terms:

200218
Industrial engineering.

LC Class. No.: T59.5

Dewey Class. No.: 620.0044

Data-driven fault diagnosis for complex industrial processestowards fault prediction, detection and identification /
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