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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.

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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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

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Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries.

fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D–S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion --- n/a --- D-S evidence theory

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This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis.

Technology: general issues --- History of engineering & technology --- process monitoring --- dynamics --- variable time lag --- dynamic autoregressive latent variables model --- sintering process --- hammerstein output-error systems --- auxiliary model --- multi-innovation identification theory --- fractional-order calculus theory --- canonical variate analysis --- disturbance detection --- power transmission system --- k-nearest neighbor analysis --- statistical local analysis --- intelligent fault diagnosis --- stacked pruning sparse denoising autoencoder --- convolutional neural network --- anti-noise --- flywheel fault diagnosis --- belief rule base --- fuzzy fault tree analysis --- Bayesian network --- evidential reasoning --- aluminum reduction process --- alumina concentration --- subspace identification --- distributed predictive control --- spatiotemporal feature fusion --- gated recurrent unit --- attention mechanism --- fault diagnosis --- evidential reasoning rule --- system modelling --- information transformation --- parameter optimization --- event-triggered control --- interval type-2 Takagi–Sugeno fuzzy model --- nonlinear networked systems --- filter --- gearbox fault diagnosis --- convolution fusion --- state identification --- PSO --- wavelet mutation --- LSSVM --- data-driven --- operational optimization --- case-based reasoning --- local outlier factor --- abnormal case removal --- bearing fault detection --- deep residual network --- data augmentation --- canonical correlation analysis --- just-in-time learning --- fault detection --- high-speed trains --- autonomous underwater vehicle --- thruster fault diagnostics --- fault tolerant control --- robust optimization --- ocean currents --- n/a --- interval type-2 Takagi-Sugeno fuzzy model

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An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice.

Technology: general issues --- History of engineering & technology --- entrained flow coal gasification --- ash melting point --- operation temperature --- Markov process --- stochastic optimization model --- genetic algorithm --- gallium nitride --- magnetic-free converters --- module-level converters --- parallel architecture --- partial shading --- photovoltaic systems --- switched capacitor converters --- hybrid energy storage system --- supercapacitor --- lead–acid battery --- energy management system --- battery degradation --- depth of discharge --- techno-economic analysis --- hybrid power station --- green island --- energy storage --- remote community --- reserves --- k-means --- probabilistic dimensioning --- dynamic dimensioning --- balancing --- wave energy converters --- deep neural networks --- renewable energy sources --- spatial planning --- sentinel satellite imagery --- permanent magnet synchronous machines --- generators --- fault detection --- demagnetization --- artificial intelligence --- data mining --- machine learning --- advanced deep learning --- windspeed forecasting --- solar irradiation forecasting --- increased RES penetration --- smart grid --- scalability --- replicability --- FLEXITRANSTORE --- Angolan economy --- diversification --- strategic alternative --- biofuels