Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science. The book Discusses basic as well as advance research in the field of prognostics. Explores integration of data collection, fault detection, degradation modeling and reliability prediction in one volume. Covers prognostics and health management (PHM) of engineering systems. Discusses latest approaches in the field of prognostics based on machine learning. The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.

General

Imprint:	Taylor & Francis
Country of origin:	United Kingdom
Series:	Advanced Research in Reliability and System Assurance Engineering
Release date:	September 2023
First published:	2024
Editors:	Gunjan Soni • Om Prakash Yadav • Gaurav Kumar Badhotiya • Mangey Ram
Dimensions:	234 x 156mm (L x W)
Pages:	246
ISBN-13:	978-1-03-205436-0
Categories:	Books Promotions
LSN:	1-03-205436-0
Barcode:	9781032054360

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