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Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis.

Technology: general issues --- rolling bearing --- performance degradation --- hybrid kernel function --- krill herd algorithm --- SVR --- acoustic-based diagnosis --- gear fault diagnosis --- attention mechanism --- convolutional neural network --- stacked auto-encoder --- weighting strategy --- deep learning --- bearing fault diagnosis --- intelligent leak detection --- acoustic emission signals --- statistical parameters --- support vector machine --- wavelet denoising --- Shannon entropy --- adaptive noise reducer --- gaussian reference signal --- gearbox fault diagnosis --- one against on multiclass support vector machine --- varying rotational speed --- fault detection and diagnosis --- faults estimation --- actuator and sensor fault --- observer design --- Takagi-Sugeno fuzzy systems --- automotive --- perception sensor --- lidar --- fault detection --- fault isolation --- fault identification --- fault recovery --- fault diagnosis --- fault detection and isolation (FDIR) --- autonomous vehicle --- model predictive control --- path tracking control --- fault detection and isolation --- braking control --- nonlinear systems --- fault tolerant control --- iterative learning control --- neural networks --- cryptography --- wireless sensor networks --- machine learning --- scan-chain diagnosis --- artificial neural network --- NARX --- control valve --- decision tree --- signature matrix --- n/a

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Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis.

Technology: general issues --- rolling bearing --- performance degradation --- hybrid kernel function --- krill herd algorithm --- SVR --- acoustic-based diagnosis --- gear fault diagnosis --- attention mechanism --- convolutional neural network --- stacked auto-encoder --- weighting strategy --- deep learning --- bearing fault diagnosis --- intelligent leak detection --- acoustic emission signals --- statistical parameters --- support vector machine --- wavelet denoising --- Shannon entropy --- adaptive noise reducer --- gaussian reference signal --- gearbox fault diagnosis --- one against on multiclass support vector machine --- varying rotational speed --- fault detection and diagnosis --- faults estimation --- actuator and sensor fault --- observer design --- Takagi-Sugeno fuzzy systems --- automotive --- perception sensor --- lidar --- fault detection --- fault isolation --- fault identification --- fault recovery --- fault diagnosis --- fault detection and isolation (FDIR) --- autonomous vehicle --- model predictive control --- path tracking control --- fault detection and isolation --- braking control --- nonlinear systems --- fault tolerant control --- iterative learning control --- neural networks --- cryptography --- wireless sensor networks --- machine learning --- scan-chain diagnosis --- artificial neural network --- NARX --- control valve --- decision tree --- signature matrix

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Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions.

History of engineering & technology --- structural health monitoring --- digital image processing --- damage --- gray level co-occurrence matrix --- self-organization map --- rolling bearings --- fault diagnosis --- multiscale entropy --- amplitude-aware permutation entropy --- random forest --- reusable launch vehicle --- thruster valve failure --- thruster fault detection --- Kalman filter --- machine vision --- machine diagnostics --- instantaneous angular speed --- SURVISHNO 2019 challenge --- video tachometer --- motion tracking --- edge detection --- parametric template modeling --- adaptive template matching --- genetic algorithm --- misalignment --- fault prediction --- combined prediction --- multivariate grey model --- quantum genetic algorithm --- least squares support vector machine --- lithium-ion battery --- battery faults --- battery safety --- battery management system --- fault diagnostic algorithms

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The topics of interest in this book include significant challenges in the BMS design of EV/HEV. The equivalent models developed for several types of integrated Li-ion batteries consider the environmental temperature and ageing effects. Different current profiles for testing the robustness of the Kalman filter type estimators of the battery state of charge are used in this book. Additionally, the BMS can integrate a real-time model-based sensor Fault Detection and Isolation (FDI) scheme for a Li-ion cell undergoing degradation, which uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters. This book will fully meet the demands of a large community of readers and specialists working in the field due to its attractiveness and scientific content with a great openness to the side of practical applicability. This covers various interesting aspects, especially related to the characterization of commercial batteries, diagnosis and optimization of their performance, experimental testing and statistical analysis, thermal modelling, and implementation of the most suitable Kalman filter type estimators of high accuracy to estimate the state of charge

Technology: general issues --- arrayed waveguide grating (AWG) --- CMOS sensor --- direct laser lithography --- fiber Bragg grating (FBG) --- lithium-ion battery --- fault detection and isolation --- sensor fault --- battery model --- battery management systems --- battery degradation --- electric vehicles --- online parameter estimation --- recursive least squares --- parallel-connected cells --- measuring test bench --- current distribution --- tab contact resistance --- battery --- ultracapacitor --- supercapacitor --- electric mobility --- electric bus --- SAFT lithium-ion battery --- Simscape model --- 3RC ECM Li-ion battery model --- state of charge --- adaptive EKF SOC estimator --- adaptive UKF SOC estimator --- particle filter SOC estimator --- ADVISOR estimate

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Cognitive building is a pioneering topic envisioning the future of our built environment. The concept of "cognitive" provides a paradigm shift that steps from the static concept of the building as a container of human activities towards the modernist vision of "machine à habiter" of Le Corbusier, where the technological content adds the capability of learning from users' behavior and environmental variables to adapt itself to achieve major goals such as user comfort, energy-saving, flexible functionality, high durability, and good maintainability. The concept is based on digital frameworks and IoT networks towards the concept of a smart city.

explainable post occupancy --- humanoid robot --- lighting simulation software --- BIM --- openBIM --- IFC --- IoT --- sensors --- cognitive buildings --- asset management --- digital twin --- BEM --- simulation modelling --- dynamic simulation --- Building Information Modelling (BIM) --- Internet of Things (IoT) --- facility management --- cyber-physical systems --- Building Management System --- Digital Twin --- Post-Occupancy Evaluations --- cognitive --- digital twins --- building lifecycle management --- artificial intelligence --- decision support --- self-learning --- optimization --- building performance simulation --- lighting simulation --- lighting quality --- visual comfort --- office field study --- evidence-based design --- building information modeling --- HVAC --- fan coil --- Internet of Things --- predictive maintenance --- fault detection --- smart building --- sustainable building --- construction projects --- BIM implementation --- stakeholders --- barriers --- construction product --- servitization --- level of evidence --- level curves --- ground slopes --- embankments --- road and rail design --- n/a