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Wavelet-Based Optimization and Numerical Computing for Fault Detection Method—Signal Fault Localization and Classification Algorithm

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Publicado en:Algorithms vol. 18, no. 4 (2025), p. 217
Autor principal: Sakovich Nikita
Otros Autores: Aksenov Dmitry, Pleshakova Ekaterina, Gataullin Sergey
Publicado:
MDPI AG
Materias:
Industrial equipment
Accuracy
Computation
Classification
Wavelet transforms
Fault lines
Fault location
Optimization techniques
Signal processing
Error functions
Adaptation
Decomposition
Numerical analysis
Data analysis
Localization
Fault detection
Numerical methods
Data compression
Wavelet analysis
Internet of Things
Seismic activity
Machine learning
Fourier transforms
Fault diagnosis
Failure analysis
Structural integrity
Neural networks
Optimization
Flexibility
Algorithms
Vibration analysis
Methods
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1999-4893
DOI:10.3390/a18040217
Fuente:Engineering Database