TY - BOOK ID - 138666119 TI - Algorithms for Fault Detection and Diagnosis AU - Ferracuti, Francesco AU - Freddi, Alessandro AU - Monteriù, Andrea PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - structural health monitoring KW - digital image processing KW - damage KW - gray level co-occurrence matrix KW - self-organization map KW - rolling bearings KW - fault diagnosis KW - multiscale entropy KW - amplitude-aware permutation entropy KW - random forest KW - reusable launch vehicle KW - thruster valve failure KW - thruster fault detection KW - Kalman filter KW - machine vision KW - machine diagnostics KW - instantaneous angular speed KW - SURVISHNO 2019 challenge KW - video tachometer KW - motion tracking KW - edge detection KW - parametric template modeling KW - adaptive template matching KW - genetic algorithm KW - misalignment KW - fault prediction KW - combined prediction KW - multivariate grey model KW - quantum genetic algorithm KW - least squares support vector machine KW - lithium-ion battery KW - battery faults KW - battery safety KW - battery management system KW - fault diagnostic algorithms UR - https://www.unicat.be/uniCat?func=search&query=sysid:138666119 AB - 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. ER -