Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems

This book provides insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems, focusing on recent research results and practical applications. It is valuable for academic researchers, practitioners, and students in the field of prognostics and health management.

Format: Hardback
Length: 281 pages
Publication date: 20 October 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the realm of big data-driven intelligent fault diagnosis and prognosis for mechanical systems, offering systematic overviews and insightful perspectives. It highlights recent research findings on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, and other relevant topics. The book's contents are highly valuable and captivating, attracting academic researchers, practitioners, and students alike in the field of prognostics and health management (PHM). It provides essential guidelines for readers to comprehend, explore, and implement the presented methodologies, fostering the further development of PHM in the era of big data.

The book addresses the critical challenges currently faced in the field of PHM, providing a comprehensive framework for understanding and addressing these challenges. It presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis, offering a deep understanding of the underlying principles and methodologies.

The book is enriched with abundant experimental validations and engineering cases, demonstrating the practical applications of the presented methodologies. These cases showcase the effectiveness and efficiency of the proposed approaches in real-world scenarios, making the book an invaluable resource for practitioners and researchers alike.

Furthermore, the book offers valuable insights into the future trends and developments in the field of PHM, providing readers with a roadmap for future research and innovation. It encourages readers to stay updated with the latest advancements and breakthroughs, enabling them to stay ahead of the curve in this rapidly evolving field.

In conclusion, this book is a must-read for anyone interested in big data-driven intelligent fault diagnosis and prognosis for mechanical systems. It offers a comprehensive and up-to-date perspective on the field, providing valuable insights and methodologies for academic researchers, practitioners, and students alike. With its rich content and practical applications, the book is poised to shape the future of PHM and contribute to the development of more efficient and reliable mechanical systems.

Weight: 612g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811691300
Edition number: 1st ed. 2023