Machine Learning and Knowledge Discovery for Engineering Systems Health Management

Neuausgabe 2011
ISBN: 978-1-4398-4178-5
Verlag: CRC PR INC

Buch, Englisch, 502 Seiten, Format (B × H): 155 mm x 236 mm, Gewicht: 1090 g

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. With contributions from many top authorities on the subject, this volume is the first to bring together the two areas of machine learning and systems health management.

Divided into three parts, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management. The first part of the text describes data-driven methods for anomaly detection, diagnosis, and prognosis of massive data streams and associated performance metrics. It also illustrates the analysis of text reports using novel machine learning approaches that help detect and discriminate between failure modes. The second part focuses on physics-based methods for diagnostics and prognostics, exploring how these methods adapt to observed data. It covers physics-based, data-driven, and hybrid approaches to studying damage propagation and prognostics in composite materials and solid rocket motors. The third part discusses the use of machine learning and physics-based approaches in distributed data centers, aircraft engines, and embedded real-time software systems.

Reflecting the interdisciplinary nature of the field, this book shows how various machine learning and knowledge discovery techniques are used in the analysis of complex engineering systems. It emphasizes the importance of these techniques in managing the intricate interactions within and between the systems to maintain a high degree of reliability.

Zielgruppe

Computer scientists, researchers in data mining and systems health management, and systems, electrical, and mechanical engineers.

Autoren/Hrsg.

Srivastava, Ashok N.

Han, Jiawei

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Zuverlässigkeitstechnik
• Technische Wissenschaften Technik Allgemein Technische Zuverlässigkeit, Sicherheitstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

Data-Driven Methods for Systems Health Management. Physics-Based Methods for Systems Health Management. Applications. Index.

Über Autor(innen)

Ashok N. Srivastava is the Principal Scientist for Data Mining and Systems Health Management at NASA. Dr. Srivastava has received many awards, including the IEEE Computer Society Technical Achievement Award, the NASA Exceptional Achievement Medal, NASA Group Achievement Awards, the IBM Golden Circle Award, and a U.S. Department of Education Merit Fellowship. His current research focuses on the development of data mining algorithms for anomaly detection in massive data streams, kernel methods in machine learning, and text mining algorithms.

Jiawei Han is an Abel Bliss Professor of Computer Science at the University of Illinois. He is also the Director of the Information Network Academic Research Center, which is supported by the U.S. Army Research Lab. A fellow of ACM and IEEE, Dr. Han has received numerous honors, including IEEE W. Wallace McDowell Award, IEEE Computer Society Technical Achievement Award, ACM SIGKDD Innovation Award, IBM Faculty awards, and HP Innovation awards. His research interests include data mining, information network analysis, and database systems.