Structural Health Monitoring Based on Data Science Techniques


Book Description

The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.




Data Driven Methods for Civil Structural Health Monitoring and Resilience


Book Description

Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.




Frequent Pattern Mining


Book Description

This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.




Intelligent Quality Assessment of Railway Switches and Crossings


Book Description

This book focuses on the latest scientific and technological advancements in the field of railway turnout engineering. It offers a holistic approach to the scientific investigation of the factors and mechanisms determining performance degradation of railway switches and crossings (S&Cs), and the consequent development of condition monitoring systems that will enable infrastructure managers to transition towards the implementation of predictive maintenance. The book is divided into three distinct parts. Part I discusses the modelling of railway infrastructure, including switch and crossing systems, while Part II focuses on metallurgical characterization. This includes the microstructure of in-field loaded railway steel and an analysis of rail screw failures. In turn, the third and final part discusses condition monitoring and asset management. Given its scope, the book is of interest to both academics and industrial practitioners, helping them learn about the various challenges characterizing this engineering domain and the latest solutions to properly address them.




Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021


Book Description

This volume contains selected papers from the Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, in Oulu, Finland, collected by editors with years of experiences in condition monitoring, signal processing, advanced reasoning and diagnostics, maintenance, risk assessment, and asset management. This work maximizes reader insights into the current trends in novel technologies and maintenance trends in industrial domains, energy production and energy conservation, mechatronics and robot technologies. These proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for condition monitoring and risk management professionals from industry and science exchange knowledge, experiences and strengthen multidisciplinary network those in the field. This book will be of benefit to academia, and industry alike.




Dynamic Modeling, Predictive Control and Performance Monitoring


Book Description

A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.




Machine Learning and Knowledge Discovery for Engineering Systems Health Management


Book Description

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.




Decision Analytics Applications in Industry


Book Description

This book presents a range of qualitative and quantitative analyses in areas such as cybersecurity, sustainability, multivariate analysis, customer satisfaction, parametric programming, software reliability growth modeling, and blockchain technology, to name but a few. It also highlights integrated methods and practices in the areas of machine learning and genetic algorithms. After discussing applications in supply chains and logistics, cloud computing, six sigma, production management, big data analysis, satellite imaging, game theory, biometric systems, quality, and system performance, the book examines the latest developments and breakthroughs in the field of science and technology, and provides novel problem-solving methods. The themes discussed in the book link contributions by researchers and practitioners from different branches of engineering and management, and hailing from around the globe. These contributions provide scholars with a platform to derive maximum utility in the area of analytics by subscribing to the idea of managing business through system sciences, operations, and management. Managers and decision-makers can learn a great deal from the respective chapters, which will help them devise their own business strategies and find real-world solutions to complex industrial problems.




Equipment Health Monitoring in Complex Systems


Book Description

This timely resource provides a practical introduction to equipment health monitoring (EHM) to ensure the cost effective operation and control of critical systems in defense, industrial, and healthcare applications. This book highlights how to frame health monitoring design applications within a system engineering process, to ensure an optimized EHM functional architecture and practical algorithm design. This book clarifies the need for intelligent diagnostics and proposed health monitoring framework. Machine learning for health monitoring, including feature extraction, data visualization, model boundaries and performance is presented. Details about monitoring aircraft engines and model based monitoring systems are described in detail. Packed with two full chapters of case studies within industrial and healthcare settings, this book identifies key problems and provides insightful techniques for solving them. This resource provides a look into the future direction in health monitoring and emerging developments within sensing technology, big data analytics, and advanced computing capabilities.