Algorithms for Fault Detection and Diagnosis
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 involv...
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| Format: | Online |
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| Idioma: | anglès |
| Publicat: |
MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Matèries: | |
| Accés en línia: | ONIX_20210501_9783036504629_240 |
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| _version_ | 1869514160734208000 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-68494 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-684942024-04-11T15:11:23Z Algorithms for Fault Detection and Diagnosis Ferracuti, Francesco Freddi, Alessandro Monteriù, Andrea 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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. 2021-05-01T15:11:20Z 2021-05-01T15:11:20Z 2021 book ONIX_20210501_9783036504629_240 9783036504629 9783036504636 https://directory.doabooks.org/handle/20.500.12854/68494 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/3514 https://mdpi.com/books/pdfview/book/3514 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-0463-6 10.3390/books978-3-0365-0463-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036504629 9783036504636 130 Basel, Switzerland open access |
| spellingShingle | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Algorithms for Fault Detection and Diagnosis |
| title | Algorithms for Fault Detection and Diagnosis |
| title_full | Algorithms for Fault Detection and Diagnosis |
| title_fullStr | Algorithms for Fault Detection and Diagnosis |
| title_full_unstemmed | Algorithms for Fault Detection and Diagnosis |
| title_short | Algorithms for Fault Detection and Diagnosis |
| title_sort | algorithms for fault detection and diagnosis |
| topic | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20210501_9783036504629_240 |