Frequency-Domain Based Fault Detection: Application of Short-Time Fourier Transform

2020 ◽  
pp. 195-221
Author(s):  
Abhisek Ukil ◽  
Yew Ming Yeap ◽  
Kuntal Satpathi
Keyword(s):  
Fourier Transform ◽  
Fault Detection ◽  
Frequency Domain ◽  
Short Time Fourier Transform ◽  
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%.

Research and analysis of the wheeled vehicle load spectrum editing method based on short-time Fourier transform

2019 ◽  
Vol 233 (14) ◽  
pp. 3671-3683
Author(s):  
Zongkai Liu ◽  
Chuan Peng ◽  
Xiaoqiang Yang
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Fatigue Damage ◽  
Time Domain ◽  
Short Time Fourier Transform ◽  
Load Spectrum ◽  
Statistical Parameters ◽  
Original Spectrum ◽  
The Time Domain ◽  
Short Time

The measured uniaxial-head load spectrum in the road simulation test has a large number of useless small loads. When applying the measured load spectrum directly, it will take a lot of time. This paper designs a comprehensive road spectrum measurement system to collect data and proposes a method for editing the uniaxial-head acceleration load spectrum using short-time Fourier transform to speed up the reliability test process and reduce time costs. In this method, the time domain and frequency domain information of the signal is obtained by short-time Fourier transform. The concept of accumulated power spectral density is proposed to identify the reduced load data, and the relative fatigue damage is used as the pass criterion. The length of the edited spectrum is only 66% of the original spectrum through the above-mentioned editing method and retains the relative damage amount of 91%. Finally, through the analysis of time domain, frequency domain, and fatigue statistical parameters, it demonstrates that the short-time Fourier transform–based acceleration load spectrum edition method could achieve a similar fatigue damage to the original spectrum in a shorter time.

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
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