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Today, we are facing a data rich world that is changing faster than ever before. The ubiquitous availability of data provides great opportunities for industrial enterprises to improve their process quality and productivity. Industrial data analytics is the process of collecting, exploring, and analyzing data generated from industrial operations and throughout the product life cycle in order to gain insights and improve decision-making. This book describes industrial data analytics approaches with an emphasis on diagnosis and prognosis of industrial processes and systems.

A large number of textbooks/research monographs exist on diagnosis and prognosis in the engineering field. Most of these engineering books focus on model-based diagnosis and prognosis problems in dynamic systems. The model-based approaches adopt a dynamic model for the system, often in the form of a state space model, as the basis for diagnosis and prognosis. Different from these existing books, this book focuses on the concept of random effects and its applications in system diagnosis and prognosis. The impetus for this book arose from the current digital revolution. In this digital age, the essential feature of a modern engineering system is that a large amount of data from multiple similar units/machines during their operations are collected in real time. This feature poses significant intellectual opportunities and challenges. As for opportunities, since we have observations from potentially a very large number of similar units, we can compare their operations, share the information, and extract common knowledge to enable accurate and tailored prediction and control at the individual level. As for challenges, because the data are collected in the field and not in a controlled environment, the data contain significant variation and heterogeneity due to the large variations in working/usage conditions for different units. This requires that the analytics approaches should be not only general (so that the common information can be learned and shared), but also flexible (so that the behavior of an individual unit can be captured and controlled). The random effects modeling approaches can exactly address these opportunities and challenges.

Random effects, as the name implies, refer to the underlying random factors in an industrial process or system that impact on the outcome of the process. In diagnosis and prognosis applications, random effects can be used to model the sources of variation in a process and the variation among individual characteristics of multiple heterogeneous units. Some excellent books are available in the industrial statistics area. However, these existing books mainly focus on population level behavior and fixed effects models. The goal of this book is to adapt and bring the theory and techniques of random effects to the application area of industrial system diagnosis and prognosis.

The book contains two main parts. The first part covers general statistical concepts and theory useful for describing and modeling variation, fixed effects, and random effects for both univariate and multivariate data, which provides the necessary background for the second part of the book. The second part covers advanced statistical methods for variation source diagnosis and system failure prognosis based on the random effects modeling approach. An appendix summarizing the basic results in linear spaces and matrix theory is also included at the end of the book for the sake of completeness.

This book is intended for students, engineers, and researchers who are interested in using modern statistical methods for variation modeling, analysis, and prediction in industrial systems. It can be used as a textbook for a graduate level or advanced undergraduate level course on industrial data analytics and/or quality and reliability engineering. We also include “Bibliographic Notes” at the end of each chapter that highlight relevant additional reading materials for interested readers. These bibliographic notes are not intended to provide a complete review of the topic. We apologize for missing literature that is relevant but not included in these notes.

Many of the materials of this book come from the authors’ recent research works in variation modeling and analysis, variation source diagnosis, and system condition and failure prognosis for manufacturing systems and beyond. We hope this book can stimulate some new research and serve as a reference book for researchers in this area.

Shiyu Zhou Madison, Wisconsin, USA Yong Chen Iowa City, Iowa, USA