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Algorithms for Fault Detection and Diagnosis
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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.
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Keywords
- adaptive template matching
- amplitude-aware permutation entropy
- battery faults
- battery management system
- battery safety
- combined prediction
- damage
- Digital Image Processing
- edge detection
- fault diagnosis
- fault diagnostic algorithms
- fault prediction
- genetic algorithm
- gray level co-occurrence matrix
- History of engineering & technology
- instantaneous angular speed
- Kalman filter
- least squares support vector machine
- lithium-ion battery
- machine diagnostics
- machine vision
- misalignment
- motion tracking
- multiscale entropy
- multivariate grey model
- parametric template modeling
- quantum genetic algorithm
- random forest
- reusable launch vehicle
- rolling bearings
- self-organization map
- Structural health monitoring
- SURVISHNO 2019 challenge
- Technology, engineering, agriculture
- Technology: general issues
- thruster fault detection
- thruster valve failure
- video tachometer
Links
DOI: 10.3390/books978-3-0365-0463-6Editions