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.
| ISBN: | 9781032054360 |
| Publication date: | 22nd September 2023 |
| Author: | Gunjan MNIT, Jaipur, India Soni |
| Publisher: | CRC Press an imprint of Taylor & Francis Ltd |
| Format: | Hardback |
| Pagination: | 246 pages |
| Series: | Advanced Research in Reliability and System Assurance Engineering |
| Genres: |
Production and industrial engineering Other manufacturing technologies Production and quality control management Automatic control engineering Machine learning |
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.
Intelligent Prognostics for Engineering Systems with Machine Learning Techniques features in the following genres: Production and industrial engineering, Other manufacturing technologies, Production and quality control management, Automatic control engineering, Machine learning
Intelligent Prognostics for Engineering Systems with Machine Learning Techniques is available in Hardback
Intelligent Prognostics for Engineering Systems with Machine Learning Techniques was written by Gunjan MNIT, Jaipur, India Soni and published by CRC Press an imprint of Taylor & Francis Ltd
Intelligent Prognostics for Engineering Systems with Machine Learning Techniques has 246 pages
Yes it is part of Advanced Research in Reliability and System Assurance Engineering series
£131.39