This book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design. Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation. Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design.

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

Data-Based Controller Design presents a comprehensive analysis of data-based control design. It brings together the different data-based design methods that have been presented in the literature since the late 1990’s. To the best knowledge of the author, these data-based design methods have never been collected in a single text, analyzed in depth or compared to each other, and this severely limits their widespread application. In this book these methods will be presented under a common theoretical framework, which fits also a large family of adaptive control methods: the MRAC (Model Reference Adaptive Control) methods. This common theoretical framework has been developed and presented very recently. The book is primarily intended for PhD students and researchers - senior or junior - in control systems. It should serve as teaching material for data-based and adaptive control courses at the graduate level, as well as for reference material for PhD theses. It should also be useful for advanced engineers willing to apply data-based design. As a matter of fact, the concepts in this book are being used, under the author’s supervision, for developing new software products in a automation company. The book will present simulation examples along the text. Practical applications of the concepts and methodologies will be presented in a specific chapter.

The projects tackled by the software development industry have grown in scale and complexity. Costs are increasing along with the number of developers. Power bills for distributed projects have reached the point where optimisations pay literal dividends. Over the last 10 years, a software development movement has gained traction, a movement founded in games development. The limited resources and complexity of the software and hardware needed to ship modern game titles demanded a different approach. Data-oriented design is inspired by high-performance computing techniques, database design, and functional programming values. It provides a practical methodology that reduces complexity while improving performance of both your development team and your product. Understand the goal, understand the data, understand the hardware, develop the solution. This book presents foundations and principles helping to build a deeper understanding of data-oriented design. It provides instruction on the thought processes involved when considering data as the primary detail of any project.

Design Methodology for Future Products – Data Driven, Agile and Flexible provides an overview of the recent research in the field of design methodology from the point of view of the members of the scientific society for product development (WiGeP - Wissenschaftliche Gesellschaft für Produktenwicklung e.V.). This book aims to contribute to design methods and their implementation for innovative future products. The main focus is the crucial data-driven, agile, and flexible way of working. Four topics are covered in corresponding chapters, Methods for Product Development and Management, Methods for Specific Products and Systems, Facing the Challenges in Product Development and Model-Based Engineering in Product Development. This publication starts with the agile strategic foresight of sustainable mechatronic and cyber-physical systems, moves on to the topics of system generation engineering in development processes, followed by the technical inheritance in data-driven product development. Product improvements are shown via agile experiential learning based on reverse engineering and via combination of usability and emotions. Furthermore, the development of future-oriented products in the field of biomechatronic systems, sustainable mobility systems and in situ sensor integration is shown. The overcoming of challenges in product development is demonstrated through context-adapted methods by focusing on efficiency and effectiveness, as well as designer-centered methods to tackle cognitive bias. Flow design for target-oriented availability of data and information in product development is addressed. Topics of model-based systems engineering are applied to the function-driven product development by linking model elements at all stages and phases of the product. The potential of model-based systems engineering for modular product families and engineering of multidisciplinary complex systems is shown.