Data Driven Technology For Engineering Systems Health Management
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Data-Driven Technology for Engineering Systems Health Management
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.
Prognostics and Health Management of Engineering Systems
This book introduces the methods for predicting the future behavior of a system’s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application.Among the many topics discussed in-depth are:• Prognostics tutorials using least-squares• Bayesian inference and parameter estimation• Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter• Data-driven prognostics algorithms including Gaussian process regression and neural network• Comparison of different prognostics algorithms divThe authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in the above fields.
Innovations for Healthcare and Wellbeing
Various socio-demographic, medical, technological, and managerial trends determine the emergence and development of the concept of the “Smart Hospital”, as well as the development and implementation of appropriate complex architectural models in the management practice of medical organizations. In turn, such medical organizations require an innovative health care ecosystem to provide medically and economically efficient healthcare services. This book examines various approaches to the modern healthcare system to provide an effective internal environment for the medical organization as well as an effective external environment for better interaction with all stakeholders of the greater healthcare system. It addresses the challenges of digital technology adoption in specialized areas (e.g., cardiology, surgery, neonatology, etc.) and of the dissemination of knowledge, technology, innovation, and entrepreneurial initiatives as well as communication between stakeholders. It then explores the development of the Smart Hospital by analyzing the internal architecture of medical organizations, key factors of their transformation, architecture of IT and digital technologies and data-driven management. Finally, this book explores the ways in which entrepreneurship and entrepreneurial leadership promote innovation and well-being in different organizational contexts, with special emphasis on human resource management, intellectual capital, and abusive leadership of public, social, and business sector contexts.