Sparse Bayesian learning beamforming combined with short-time Fourier transform for fault detection of wind turbine blades

2019 ◽  
Vol 145 (3) ◽  
pp. 1802-1802 ◽  
Author(s):  
Xiang Pan ◽  
Zefeng Cheng ◽  
Zheng Zheng ◽  
Yehui Zhang
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Wind Turbine ◽  
Bayesian Learning ◽  
Turbine Blades ◽  
Sparse Bayesian Learning ◽  
Short Time Fourier Transform ◽  
Wind Turbine Blades ◽  
Short Time

Fourier Transform and Short-Time Fourier Transform Decomposition for Photovoltaic Arc Fault Detection

2020 ◽  
Author(s):  
Rahul Balamurugan ◽  
Fatima Al-Janahi ◽  
Oumaima Bouhali ◽  
Sawsan Shukri ◽  
Kais Abdulmawjood ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Short Time Fourier Transform ◽  
Short Time

scholarly journals Fault Detection in DC Microgrids Using Short-Time Fourier Transform

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 277
Author(s):  
Ivan Grcić ◽  
Hrvoje Pandžić ◽  
Damir Novosel
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Operating Conditions ◽  
Time Signal ◽  
Short Time Fourier Transform ◽  
Dc Microgrid ◽  
Protection Scheme ◽  
Simulink Model ◽  
Battery Energy ◽  
Short Time

Fault detection in microgrids presents a strong technical challenge due to the dynamic operating conditions. Changing the power generation and load impacts the current magnitude and direction, which has an adverse effect on the microgrid protection scheme. To address this problem, this paper addresses a field-transform-based fault detection method immune to the microgrid conditions. The faults are simulated via a Matlab/Simulink model of the grid-connected photovoltaics-based DC microgrid with battery energy storage. Short-time Fourier transform is applied to the fault time signal to obtain a frequency spectrum. Selected spectrum features are then provided to a number of intelligent classifiers. The classifiers’ scores were evaluated using the F1-score metric. Most classifiers proved to be reliable as their performance score was above 90%.

A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF

2020 ◽  
Vol 20 (4) ◽  
pp. 2023-2033 ◽  
Author(s):  
Milad Rezamand ◽  
Mojtaba Kordestani ◽  
Rupp Carriveau ◽  
David S.-K. Ting ◽  
Mehrdad Saif
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Detection Method ◽  
Turbine Blades ◽  
Wind Turbine Blades

scholarly journals A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors

2021 ◽  
Vol 3 (1) ◽  
pp. 228-242
Author(s):  
Christos T. Alexakos ◽  
Yannis L. Karnavas ◽  
Maria Drakaki ◽  
Ioannis A. Tziafettas
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Image Classification ◽  
Rolling Element Bearings ◽  
Short Time Fourier Transform ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Rolling Element ◽  
Computational Resources ◽  
Short Time

The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolutional neural networks (CNNs) have been proposed for bearing fault detection and identification. However, two major drawbacks of these models are (a) their lack of ability to capture global information about the input vector and to derive knowledge about the statistical properties of the latter and (b) the high demand for computational resources. In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. To diagnose and classify the vibration images, the image classification transformer (ICT), inspired from the transformers used for natural language processing, has been suitably adapted to work as an image classifier trained in a supervised manner and is also proposed as an alternative method to CNNs. Simulation results on a famous and well-established rolling element bearing fault detection benchmark show the effectiveness of the proposed method, which achieved 98.3% accuracy (on the test dataset) while requiring substantially fewer computational resources to be trained compared to the CNN approach.

Analysis of the influence of the window used in the Short-Time Fourier Transform for High Impedance Fault detection

2016 ◽  
Author(s):  
Érica Mangueira Lima ◽  
Nubia Silva Dantas Brito ◽  
Benemar Alencar de Souza ◽  
Wellinsilvio Costa dos Santos ◽  
Lais Martins de Andrade Fortunato
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Short Time Fourier Transform ◽  
High Impedance ◽  
High Impedance Fault ◽  
Short Time

scholarly journals Application of Short-time Fourier Transform in Feeder Fault Detection of Flexible Multi-state Switch

2021 ◽  
Vol 1754 (1) ◽  
pp. 012106
Author(s):  
Yimin Cheng ◽  
Guorong Zhang ◽  
Chong Yu ◽  
Bo Peng ◽  
Xuewei Wu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Short Time Fourier Transform ◽  
Short Time

High impedance fault detection method based on the short-time Fourier transform

2018 ◽  
Vol 12 (11) ◽  
pp. 2577-2584 ◽  
Author(s):  
Érica Mangueira Lima ◽  
Caio Marco dos Santos Junqueira ◽  
Núbia Silva Dantas Brito ◽  
Benemar Alencar de Souza ◽  
Rodrigo de Almeida Coelho ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Detection Method ◽  
Short Time Fourier Transform ◽  
High Impedance ◽  
High Impedance Fault ◽  
Short Time
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