This book provides a comprehensive set of characterization, prediction,
optimization, evaluation, and evolution techniques for a diagnosis
system for fault isolation in large electronic systems. Readers with a
background in electronics design or system engineering can use this book
as a reference to derive insightful knowledge from data analysis and use
this knowledge as guidance for designing reasoning-based diagnosis
systems. Moreover, readers with a background in statistics or data
analytics can use this book as a practical case study for adapting data
mining and machine learning techniques to electronic system design and
diagnosis. This book identifies the key challenges in reasoning-based,
board-level diagnosis system design and presents the solutions and
corresponding results that have emerged from leading-edge research in
this domain. It covers topics ranging from highly accurate fault
isolation, adaptive fault isolation, diagnosis-system robustness
assessment, to system performance analysis and evaluation, knowledge
discovery and knowledge transfer. With its emphasis on the above topics,
the book provides an in-depth and broad view of reasoning-based fault
diagnosis system design.
- Explains and applies optimized techniques from the machine-learning
domain to solve the fault diagnosis problem in the realm of electronic
system design and manufacturing;- Demonstrates techniques based on
industrial data and feedback from an actual manufacturing line;-
Discusses practical problems, including diagnosis accuracy, diagnosis
time cost, evaluation of diagnosis system, handling of missing syndromes
in diagnosis, and need for fast diagnosis-system development.
