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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.
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.
This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.
Traditionally fatigue, fracture, damage mechanics are predictions are based on empirical curve fitting models based on experimental data. However, when entropy is used as the metric for degradation of the material, the modeling process becomes physics based rather than empirical modeling. Because, entropy generation in a material can be calculated from the fundamental equation of thematerial. This collection of manuscripts is about using entropy for "Fatigue, Fracture, Failure Prediction and Structural Health Monitoring". The theoretical paper in the collection provides the mathematical and physics framework behind the unified mechanics theory, which unifies universal laws of motion of Newton and laws of thermodynamics at ab-initio level. Unified Mechanics introduces an additional axis called, Thermodynamic State Index axis which is linearly independent from Newtonian space x, y, z and time. As a result, derivative of displacement with respect to entropy is not zero, in unified mechanics theory, as in Newtonian mechanics. Any material is treated as a thermodynamic system and fundamental equation of the material is derived. Fundamental equation defines entropy generation rate in the system. Experimental papers in the collection prove validity of using entropy as a stable metric for Fatigue, Fracture, Failure Prediction and Structural Health Monitoring.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding. Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc. - Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things - Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things - Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more
Industrial Prognostics predicts an industrial system’s lifespan using probability measurements to determine the way a machine operates. Prognostics are essential in determining being able to predict and stop failures before they occur. Therefore the development of dependable prognostic procedures for engineering systems is important to increase the system’s performance and reliability. Diagnostics and Prognostics of Engineering Systems: Methods and Techniques provides widespread coverage and discussions on the methods and techniques of diagnosis and prognosis systems. Including practical examples to display the method’s effectiveness in real-world applications as well as the latest trends and research, this reference source aims to introduce fundamental theory and practice for system diagnosis and prognosis.
Patients with unmet needs will continue to increase as no viable nor adequate treatment exists. Meanwhile, healthcare systems are struggling to cope with the rise of patients with chronic diseases, the ageing population and the increasing cost of drugs. What if there is a faster and less expensive way to provide better care for patients using the right digital solutions and transforming the growing volumes of health data into insights? The increase of digital health has grown exponentially in the last few years. Why is there a slow uptake of these new digital solutions in the healthcare and pharmaceutical industries? One of the key reasons is that patients are often left out of the innovation process. Their data are used without their knowledge, solutions designed for them are developed without their input and healthcare professionals refuse their expertise. This book explores what it means to empower patients in a digital world and how this empowerment will bridge the gap between science, technology and patients. All these components need to co-exist to bring value not only to the patients themselves but to improve the healthcare ecosystem. Patients have taken matters into their own hands. Some are equipped with the latest wearables and applications, engaged in improving their health using data, empowered to make informed decisions and ultimately are experts in their disease(s). They are the e-patients. The other side of the spectrum are patients with minimal digital literacy but equally willing to donate their data for the purpose of research. Finding the right balance when using digital health solutions becomes as critical as the need to develop a disease-specific solution. For the first time, the authors look at healthcare and technologies through the lens of patients and physicians via surveys and interviews in order to understand their perspective on digital health, analyse the benefits for them, explore how they can actively engage in the innovation process, and identify the threats and opportunities the large volumes of data create by digitizing healthcare. Are patients truly ready to know everything about their health? What is the value of their data? How can other stakeholders join the patient empowerment movement? This unique perspective will help us re-design the future of healthcare - an industry in desperate need for a change.
This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. The book presents the most noteworthy methods and results discussed at the International Conference on Reliability and Statistics in Transportation and Communication (RelStat), which took place in Riga, Latvia on October 16 – 19, 2019. It spans a broad spectrum of topics, from mathematical models and design methodologies, to software engineering, data security and financial issues, as well as practical problems in technical systems, such as transportation and telecommunications, and in engineering education.