Wyniki wyszukiwania - BazTech

1

High impedance fault detection in low voltage overhead distribution based wavelet and harmonic indices

Albanna Enaam, Al-Rifaie Alya Hamid, Al-Karakchi Ahmed A. Abdullah

Przegląd Elektrotechniczny

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2023

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R. 99, nr 7

243--246

EN

High-impedance fault HIF occurs when an energized conductor makes contact with a surface with a high impedance. Conventional overcurrent protection cannot detect this fault due to the low fault current, and there is no effective protection for HIFs. This paper introduces a novel method for detecting HIFs in low voltage distribution systems by decomposing neutral current using Wavelet and FFT. Modeling HIF fault data in Matlab to analyze the proposed scheme. Simulations demonstrate that the proposed method can accurately detect HIF and distinguish it.

PL

Błąd wysokiej impedancji HIF występuje, gdy przewodnik pod napięciem styka się z powierzchnią o wysokiej impedancji. Konwencjonalne zabezpieczenie nadprądowe nie jest w stanie wykryć tej usterki z powodu niskiego prądu zwarciowego i nie ma skutecznej ochrony dla HIF. W artykule przedstawiono nowatorską metodę wykrywania HIF w systemach dystrybucji niskiego napięcia poprzez dekompozycję prądu neutralnego za pomocą funkcji Wavelet i FFT. Modelowanie danych o błędach HIF w Matlabie w celu analizy proponowanego schematu. Symulacje pokazują, że proponowana metoda może dokładnie wykrywać i rozróżniać HIF.

2

VMD and CNN-Based Classification Model for Infrasound Signal

Lu Quanbo, Li Mei

Archives of Acoustics

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2023

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Vol. 48, No. 3

403--412

EN

Infrasound signal classification is vital in geological hazard monitoring systems. The traditional classification approach extracts the features and classifies the infrasound events. However, due to the manual feature extraction, its classification performance is not satisfactory. To deal with this problem, this paper presents a classification model based on variational mode decomposition (VMD) and convolutional neural network (CNN). Firstly, the infrasound signal is processed by VMD to eliminate the noise. Then fast Fourier transform (FFT) is applied to convert the reconstructed signal into a frequency domain image. Finally, a CNN model is established to automatically extract the features and classify the infrasound signals. The experimental results show that the classification accuracy of the proposed classification model is higher than the other model by nearly 5%. Therefore, the proposed approach has excellent robustness under noisy environments and huge potential in geophysical monitoring.

3

Application of the Acoustic Emission Technique for Damage Identification in the Fiber Reinforced Polymer Composites

Samborski Sylwester, Korzec Izabela

Advances in Science and Technology. Research Journal

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2023

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Vol. 17, no 1

210--221

EN

A set of experiments having in target determination of fracture resistance was performed on the Fiber Reinforced Polymer (FRP) composites specimens with an additional monitoring of damage onset and evolution with a so-called Acoustic Emission (AE) technique. The AE technique is a non-destructive material testing method, which enables registering the phenomena usually not audible with a human ear - the frequency bands lay between 100 and 1000kHz. For the FRP composites this enables monitoring various damage phenomena - matrix cracking, delamination, fiber cracking etc. by acquisition and subsequent analysis of several AE parameters: number of hits, number of counts, amplitude or energy of the signal. In the paper advantages of a deeper analysis of the raw AE signal was presented with an application of the Fast Fourier Transform (FFT), leading to a more detailed damage identification along the whole loading procedure. The study proved the usability of the AE method in damage monitoring of the FRPs; a bundle of illustrative examples of chosen acoustic emission parameters’ evolution displayed on the background of the load applied to composite specimens was presented and interpreted.

4

Fault Analysis of Worm Gear Box Using Symlets Wavelet

Thamba Narendiranath Babu, Thatikonda Venkata Kiran Kamesh, Nutakki Sathvik, Duraiswamy Rama Prabha, Mohammed Noor, Wahab Razia Sultana, Mangalaraja Ramalinga Viswanathan, Manivannan Ajay Vannan

Archives of Acoustics

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2020

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Vol. 45, No. 3

521--540

EN

This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%.

5

Experimental Comparative Investigations to Evaluate Cavitation Conditions within a Centrifugal Pump Based on Vibration and Acoustic Analyses Techniques

Al-Obaidi Ahmed Ramadhan

Archives of Acoustics

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2020

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Vol. 45, No. 3

541--556

EN

Cavitation is an essential problem that occurs in all kinds of pumps. This cavitation contributes highly towards the deterioration in the performance of the pump. In industrial applications, it is very vital to detect and decrease the effect of the cavitation in pumps. Using different techniques to analysis and diagnose cavitation leads to increase in the reliability of cavitation detection. The use of various techniques such as vibration and acoustic analyses can provide a more robust detection of cavitation within the pump. In this work therefore, focus is put on detecting and diagnosing the cavitation phenomenon within a centrifugal pump using vibration and acoustic techniques. The results obtained from vibration and acoustic signals in time and frequency domains were analysed in order to achieve better understanding regarding detection of cavitation within a pump. The effect of different operating conditions related to the cavitation was investigated in this work using different statistical features in time domain analysis (TDA). Moreover, Fast Fourier Transform (FFT) technique for frequency domain analysis (FDA) was also applied. Furthermore, the comparison and evaluation system among different techniques to find an adequate technique incorporating for accuracy and to increase the reliability of detection and diagnosing different levels of cavitation within a centrifugal pump were also investigated.

6

Identification of Bee Colony Acoustic Signals using the Dynamic Time Warping Algorithm

Polyniak Yu., Fedasyuk D., Marusenkova Tetyana

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

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2019

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Vol. 8, No 4

19--27

EN

The work deals with automated recognition of the current state of a bee colony, for continuous monitoring of processes running in a bee hive is of key importance in beekeeping. The dynamic time warping algorithm is considered as a method of analyzing acoustic signals produced by a bee colony. Upon such an analysis one can make inferences about the current state of the colony. We have developed a software module for audio-signal identification, which is to be used as a part of an automated bee colony monitoring system, and a software tool for verification of the module. We evaluated the efficacy of the algorithm, the probability of bee colony states correctly recognized using acoustic signals produced by the colony and consumed computational resources by the example of a queen bee’s sounds recorded during swarming. The dependencies of the signal processing time and the successful pattern recognition probabilities on the frame sample rate and frame size are presented.

7

Modeling of anisotropic hardening and grain size effects based on advanced numerical methods and crystal plasticity

Berisha B., Hirsiger S., Hippke H., Hora P., Mariaux A., Leyvraz D., Bezençon C.

Archives of Mechanics

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2019

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Vol. 71, nr 4-5

489--505

EN

Modeling of anisotropic behavior as well as hardening behavior based on micromechanical quantities in combination with a spectral solver is the focus of this study. A deep drawing steel as well as two different aluminum alloys are investigated. Prediction capabilities of the proposed modeling strategy are discussed and the benefits of the micromechanical model are highlighted. Further, a comparison of the crystal plasticity (CP) results with the well established macroscopic model YLD2000-2d underlines the importance of the CP as a complementary modeling technique to the macroscopic modeling. Both models – the microscopic as well as the macroscopic – are validated on experimental data mainly gained from uniaxial and biaxial tests. In the second part of this study, strong inhomogeneous microstructures are investigated from a modeling point of view. For this purpose, a Hall–Petch phenomenological model is implemented in the CP open-source code DAMASK to take the grain size effects into account. Appropriate combinations of the grain sizes in a bimodal microstructure are presented in order to increase the strength as well as ductility of a generic aluminium alloy. The proposed numerical strategy of coupling the CP and efficient FFT-based spectral solver supports the development of new materials in an optimal way.

8

Time–frequency Analysis of the EMG Digital Signals

Kuniszyk-Jóźkowiak W., Jaszczuk J., Sacewicz T., Codello I.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

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2012

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Vol. 12, no. 2

20--25

EN

In the article comparison of time-frequency spectra of EMG signals obtained by the following methods: Fast Fourier Transform, predictive analysis and wavelet analysis is presented. The EMG spectra of biceps and triceps while an adult man was flexing his arm were analysed. The advantages of the predictive analysis were shown as far as averaging of the spectra and determining the main maxima are concerned. The Continuous Wavelet Transform method was applied, which allows for the proper distribution of the scales, aiming at an accurate analysis and localisation of frequency maxima as well as the identification of impulses which are characteristic of such signals (bursts) in the scale of time. The modified Morlet wavelet was suggested as the mother wavelet. The wavelet analysis allows for the examination of the changes in the frequency spectrum in particular stages of the muscle contraction. Predictive analysis may also be very useful while smoothing and averaging the EMG signal spectrum in time.