Intelligent Prognostics for Engineering Systems with Machine Learning Techniques
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Overview
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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Details
- ISBN-13: 9781032054360
- ISBN-10: 1032054360
- Publisher: CRC Press
- Publish Date: September 2023
- Dimensions: 9.21 x 6.14 x 0.63 inches
- Shipping Weight: 1.2 pounds
- Page Count: 246
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