1st Edition

Machine Learning and Knowledge Discovery for Engineering Systems Health Management




ISBN 9781439841785
Published November 16, 2011 by Chapman and Hall/CRC
502 Pages 107 B/W Illustrations

USD $145.00

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Book Description

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.

Table of Contents

Data-Driven Methods for Systems Health Management
Mining Data Streams: Systems and Algorithms, Charu C. Aggarwal and Deepak S. Turaga
A Tutorial on Bayesian Networks for Systems Health Management, Arthur Choi, Lu Zheng, Adnan Darwiche, and Ole J. Mengshoel
Anomaly Detection in a Fleet of Systems, Nikunj Oza and Santanu Das
Discriminative Topic Models, Hanhuai Shan, Amrudin Agovic, and Arindam Banerjee
Prognostic Performance Metrics, Kai Goebel, Abhinav Saxena , Sankalita Saha, Bhaskar Saha, and Jose Celaya

Physics-Based Methods for Systems Health Management
Gaussian Process Damage Prognosis under Random and Flight Profile Fatigue Loading, Aditi Chattopadhyay and Subhasish Mohanty
Bayesian Analysis for Fatigue Damage Prognostics and Remaining Useful Life Prediction, Xuefei Guan and Yongming Liu
Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight, V.N. Smelyanskiy, D.G. Luchinsky, V. Hafiychuk, V.V. Osipov, I. Kulikov, and A. Patterson-Hine
Model-Based Tools and Techniques for Real-Time System and Software Health Management, Sherif Abdelwahed, Abhishek Dubey, Gabor Karsai, and Nag Mahadevan

Applications
Real-Time Identification of Performance Problems in Large Distributed Systems, Moises Goldszmidt, Dawn Woodard, and Peter Bodik
A Combined Model-Based and Data-Driven Prognostic Approach for Aircraft System Life Management, Marcos Orchard, George Vachtsevanos, and Kai Goebel
Hybrid Models for Engine Health Management, Allan J. Volponi and Ravi Rajamani
Extracting Critical Information from Free Text Data for Systems Health Management, Anne Kao, Stephen Poteet, and David Augustine

Index

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Editor(s)

Biography

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.