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.
Preface
Part I : Data-Driven Methods for Systems Health Management
Chapter 1 : Mining Data Streams: Systems and Algorithms
Chapter 2 : A Tutorial on Bayesian Networks for Systems Health Management
Chapter 3 : Anomaly Detection in a Fleet of Systems
Chapter 4 : Discriminative Topic Models
Chapter 5 : Prognostic Performance Metrics
Part II : Physics-Based Methods for Systems Health Management
Chapter 6 : Gaussian Process Damage Prognosis under Random and Flight Profile Fatigue Loading
Chapter 7 : Bayesian Analysis for Fatigue Damage Prognostics and Remaining Useful Life Prediction
Chapter 8 : Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight
Chapter 9 : Model-Based Tools and Techniques for Real-Time System and Software Health Management
Part III : Applications
Chapter 10 : Real-Time Identification of Performance Problems in Large Distributed Systems
Chapter 11 : A Combined Model-Based and Data-Driven Prognostic Approach for Aircraft System Life Management
Chapter 12 : Hybrid Models for Engine Health Management
Chapter 13 : Extracting Critical Information from Free Text Data for Systems Health Management
Index
