1st Edition
Machine Learning and Knowledge Discovery for Engineering Systems Health Management
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
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.
