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Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

By: (Edited by) Gaurav Kumar Badhotiya , (Edited by) Gunjan Soni , (Edited by) Mangey Ram , (Edited by) Om Prakash Yadav

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Ksh 22,500.00

Format: Hardback or Cased Book

ISBN-10: 1032054360

ISBN-13: 9781032054360

Series: Advanced Research in Reliability and System Assurance Engineering

Publisher: Taylor & Francis Ltd

Imprint: CRC Press

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Sep 22nd, 2023

Print length: 246 Pages

Weight: 453 grams

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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

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.


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