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Preface
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
