Shulph Ink
Intelligent Prognostics for Engineering Systems with Machine Learning Techniques
Intelligent Prognostics for Engineering Systems with Machine Learning Techniques
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- More about Intelligent Prognostics for Engineering Systems with Machine Learning Techniques
The book discusses the latest data-driven, physics-based, and hybrid approaches employed in industrial prognostics and reliability estimation. It is a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science. It also covers prognostics and health management (PHM) of engineering systems and discusses latest approaches in the field based on machine learning.
Format: Hardback
Length: 246 pages
Publication date: 22 September 2023
Publisher: Taylor & Francis Ltd
The book delves into the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It serves as a valuable resource for senior undergraduate, graduate students, and academic researchers in fields such as industrial and production engineering, electrical engineering, and computer science.
The book explores the integration of data collection, fault detection, degradation modeling, and reliability prediction, providing a comprehensive overview of prognostics and health management (PHM) of engineering systems. It discusses the latest approaches in prognostics, leveraging machine learning techniques to predict, extrapolate, and forecast process behavior based on current health state assessment and future operating conditions.
The text deals with the tools and techniques used to predict, extrapolate, and forecast the process behavior, based on current health state assessment and future operating conditions, with the help of machine learning. It serves as a useful reference text for senior undergraduate, graduate students, and academic researchers in industrial and production engineering, manufacturing science, electrical engineering, and computer science.
Weight: 640g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032054360
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