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This book is intended for students, engineers, and researchers who are interested in using modern statistical methods for variation modeling, diagnosis, and prediction in industrial systems.

This book can be used as a textbook for a graduate level or advanced undergraduate level courses on industrial data analytics. The book is fairly self-contained, although background in basic probability and statistics such as the concept of random variable, probability distribution, moments, and basic knowledge in linear algebra such as matrix operations and matrix decomposition would be useful. The appendix at the end of the book provides a summary of the necessary concepts and results in linear space and matrix theory. The materials in Part II of the book are relatively independent. So the instructor could combine selected chapters in Part II with Part I as the basic materials for different courses. For example, topics in Part I can be used for an advanced undergraduate level course on introduction to industrial data analytics. The materials in Part I and some selected chapters in Part II (e.g., Chapters 7, 8, and 9) can be used in a master’s level statistical quality control course. Similarly, materials in Part I and selected later chapters in Part II (e.g., Chapters 10, 11, 12) can be used in a master’s level course with emphasis on prognosis and reliability applications. Finally, Part II alone can be used as the textbook for an advanced graduate level course on diagnosis and prognosis.

One important feature of this book is that we provide detailed descriptions of software implementation for most of the methods and algorithms. We adopt the statistical programming language R in this book. R language is versatile and has a very large number of up-to-date packages implementing various statistical methods [R Core Team, 2020]. This feature makes this book fit well with the needs of practitioners in engineering fields to self study and implement the statistical modeling and analysis methods. All the R codes and data sets used in this book can be found at the book companion website.