Wavelet-Based Optimization and Numerical Computing for Fault Detection Method—Signal Fault Localization and Classification Algorithm
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| Опубликовано в:: | Algorithms vol. 18, no. 4 (2025), p. 217 |
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| Главный автор: | |
| Другие авторы: | , , |
| Опубликовано: |
MDPI AG
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 024 | 7 | |a 10.3390/a18040217 |2 doi | |
| 035 | |a 3194485112 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231333 |2 nlm | ||
| 100 | 1 | |a Sakovich Nikita |u Financial University under the Government of the Russian Federation, Moscow 109456, Russia; 217556@edu.fa.ru (N.S.); daaksenov@fa.ru (D.A.) | |
| 245 | 1 | |a Wavelet-Based Optimization and Numerical Computing for Fault Detection Method—Signal Fault Localization and Classification Algorithm | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study focuses on the development of the WONC-FD (Wavelet-Based Optimization and Numerical Computing for Fault Detection) algorithm for the accurate detection and categorization of faults in signals using wavelet analysis augmented with numerical methods. Fault detection is a key problem in areas related to seismic activity analysis, vibration assessment of industrial equipment, structural integrity control, and electrical grid reliability. In the proposed methodology, wavelet transform serves to accurately localize anomalies in the data, and optimization techniques are introduced to refine the classification based on minimizing the error function. This not only improves the accuracy of fault identification but also provides a better understanding of its nature. | |
| 653 | |a Industrial equipment | ||
| 653 | |a Accuracy | ||
| 653 | |a Computation | ||
| 653 | |a Classification | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Fault lines | ||
| 653 | |a Fault location | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Signal processing | ||
| 653 | |a Error functions | ||
| 653 | |a Adaptation | ||
| 653 | |a Decomposition | ||
| 653 | |a Numerical analysis | ||
| 653 | |a Data analysis | ||
| 653 | |a Localization | ||
| 653 | |a Fault detection | ||
| 653 | |a Numerical methods | ||
| 653 | |a Data compression | ||
| 653 | |a Wavelet analysis | ||
| 653 | |a Internet of Things | ||
| 653 | |a Seismic activity | ||
| 653 | |a Machine learning | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Fault diagnosis | ||
| 653 | |a Failure analysis | ||
| 653 | |a Structural integrity | ||
| 653 | |a Neural networks | ||
| 653 | |a Optimization | ||
| 653 | |a Flexibility | ||
| 653 | |a Algorithms | ||
| 653 | |a Vibration analysis | ||
| 653 | |a Methods | ||
| 700 | 1 | |a Aksenov Dmitry |u Financial University under the Government of the Russian Federation, Moscow 109456, Russia; 217556@edu.fa.ru (N.S.); daaksenov@fa.ru (D.A.) | |
| 700 | 1 | |a Pleshakova Ekaterina |u MIREA—Russian Technological University, 78 Vernadsky Avenue, Moscow 119454, Russia | |
| 700 | 1 | |a Gataullin Sergey |u Central Economics and Mathematics Institute of the Russian Academy of Sciences, Nakhimovsky Prospect, 47, Moscow 117418, Russia; sgataullin@cemi-ras.ru | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 4 (2025), p. 217 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194485112/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194485112/fulltextwithgraphics/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194485112/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |