Algorithms for Fault Detection and Diagnosis

• Autore: Ferracuti Francesco
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 electronic resource (130 p.)
• Soggetto topico: History of engineering & technology
• Soggetto non controllato: 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
• Persona (resp. second.): FreddiAlessandro; MonteriùAndrea; FerracutiFrancesco
• Sommario/riassunto: 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.
• Titolo autorizzato: Algorithms for Fault Detection and Diagnosis
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557630403321
• Lo trovi qui: Univ. Federico II