Prognostics and Health Management for Intelligent Electromechanical Systems
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Beschreibung
This book gives a detailed introduction to the technical background, feature extraction methods, PHM models and big data embedding methods of the big data theory in PHM for intelligent electromechanical systems. Combination with deep learning and big data, this book explains the hybrid algorithm framework of PHM such as ensemble intelligence and optimized intelligence and introduces PHM models for bearing, IGBT, MOSFET and other components and their big data embedding platform. This book improves the PHM method and theory of electromechanical system under industrial big data and provides reference for the development of intelligent electromechanical equipment and intelligent industrial production in the future.
This book gives a detailed introduction to the technical background, feature extraction methods, PHM models and big data embedding methods of the big data theory in PHM for intelligent electromechanical systems. Combination with deep learning and big data, this book explains the hybrid algorithm framework of PHM such as ensemble intelligence and optimized intelligence and introduces PHM models for bearing, IGBT, MOSFET and other components and their big data embedding platform. This book improves the PHM method and theory of electromechanical system under industrial big data and provides reference for the development of intelligent electromechanical equipment and intelligent industrial production in the future.
Evaluates the existing data science technologies by case study Introduces the state-of-art data science methods in PHM Provides a detailed introduction and analysis of 6 different kinds of data science technologies
Autor*in
Hui Liu
Themen in »Prognostics and Health Management for Intelligent Electromechanical Systems«
Data science Prognostics and Health Management Intelligent Diagnosis Artificial intelligent Deep learning