Wyniki wyszukiwania - BazTech

1

Inter-turn short-circuit fault diagnosis of high-voltage line-start permanent magnet synchronous motor based on improved wavelet packet energy analysis

Yang Cunxiang, Zhang Shizheng, Wang Yunzhuang, Qiu Hongbo, Zhao Hewei

Archives of Electrical Engineering

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2025

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Vol. 74, nr 4

885--898

EN

High-voltage line-start permanent magnet synchronous motors (HVLSPMSMs) are prone to inter-turn short-circuit faults, which not only result in a significant increase in current but also exacerbate motor vibration. To accurately identify the frequency-domain fault characteristics of the motor under inter-turn short-circuit conditions, an improved wavelet packet energy gain ratio analysis method is proposed. Firstly, a two-dimensional transient finite element simulation model is established, and the validity of the model is verified through experimental data. On this basis, the inter-turn short-circuit fault state of the motor is further analyzed, and the corresponding fault signals are extracted. Secondly, the improved wavelet packet transform (IWPT) is applied to analyze the fault current and vibration signals. By combining the energy gain ratio, fast Fourier transform (FFT) is conducted on the sensitive wavelet packet coefficient nodes to extract the fault characteristics of the current and vibration signals by comparing the frequencies before and after the fault occurrence. Finally, a comparison with traditional wavelet packet analysis demonstrates the reliability and accuracy of the proposed method.

2

Optimal mother wavelet selection for a stochastic resynthesis of the sound textures

Wronka Wojciech

Vibrations in Physical Systems

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2023

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Vol. 34, nr 2

art. no. 2023218

EN

Continuous wavelet transform is a powerful and versatile tool for signal analysis, outperforming short-time Fourier transform in the task of non-stationary, transient signals analysis. However, the method’s performance is heavily influenced by the choice of a mother wavelet function, which is most often made by the experience-supported intuition, followed by the trial-and-error procedure. Numerous attempts to optimize the problem are not universal by any means, as its solution is determined by a particular application, acquired data, and other system requirements. One very specific example is wavelet-based statistical analysis, performed for the needs of the stochastic resynthesis of sound textures, which requires minimal decomposition and precise time localization of the individual acoustic events, components of a complex texture. This work presents the automated mother wavelet function optimization system, which performs the optimal selection based on the reference audio signal. The algorithm iterates through a wide set of commonly used functions and compares the wavelet packet decomposition trees in search of the single node, containing the most information possible, with the use of the entropy-based criterion. After performing the procedure, reference signal is resynthesized with coefficients of the selected wavelet function and then calculation of normalized root mean square error serves as a verification of the results. Conclusions contain both the advantages and the limitations of the proposed solution together with the possible improvements and the directions of future research.

3

Sleep Snoring Sound Recognition Based on Wavelet Packet Transform

Ding Li, Peng Jianxin, Zhang Xiaowen, Song Lijuan

Archives of Acoustics

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2023

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Vol. 48, No. 1

3--12

EN

Snoring is a typical and intuitive symptom of the obstructive sleep apnea hypopnea syndrome (OSAHS), which is a kind of sleep-related respiratory disorder having adverse effects on people’s lives. Detecting snoring sounds from the whole night recorded sounds is the first but the most important step for the snoring analysis of OSAHS. An automatic snoring detection system based on the wavelet packet transform (WPT) with an eXtreme Gradient Boosting (XGBoost) classifier is proposed in the paper, which recognizes snoring sounds from the enhanced episodes by the generalization subspace noise reduction algorithm. The feature selection technology based on correlation analysis is applied to select the most discriminative WPT features. The selected features yield a high sensitivity of 97.27% and a precision of 96.48% on the test set. The recognition performance demonstrates that WPT is effective in the analysis of snoring and non-snoring sounds, and the difference is exhibited much more comprehensively by sub-bands with smaller frequency ranges. The distribution of snoring sound is mainly on the middle and low frequency parts, there is also evident difference between snoring and non-snoring sounds on the high frequency part.

4

Prediction of Psychoacoustic Metrics Using Combination of Wavelet Packet Transform and an Optimized Artificial Neural Network

Pourseiedrezaei Mehdi, Loghmani Ali, Keshmiri Mehdi

Archives of Acoustics

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2019

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Vol. 44, No. 3

561--573

EN

In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.

5

Noise Detection for Biosignals Using an Orthogonal Wavelet Packet Tree Denoising Algorithm

Schimmack M., Mercorelli P.

International Journal of Electronics and Telecommunications

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2016

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Vol. 62, No. 1

15--21

EN

This article deals with the noise detection of discrete biosignals using an orthogonal wavelet packet. In specific, it compares the usefulness of Daubechies wavelets with different vanishing moments for the denoising and compression of the digitalised biosignals in case of surface electromyography (sEMG) signals. The work is based upon the discrete wavelet transform (DWT) version of wavelet package transform (WPT). A noise reducing algorithm is proposed to detect unavoidable noise in the acquired data in a model independent way. The noise of a signal sequence will be defined by a seminorm. This method was developed for a possible observation during a fracture healing period. The proposed method is general for signal processing and its design was based upon the wavelet packet.

6

Hierarchical classification of normal, fatty and heterogeneous liver diseases from ultrasound images using serial and parallel feature fusion

Alivar A., Danyali H., Helfroush M. S.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

697--707

EN

This study presents a computer-aided diagnostic system for hierarchical classification of normal, fatty, and heterogeneous liver ultrasound images using feature fusion techniques. Both spatial and transform domain based features are used in the classification, since they have positive effects on the classification accuracy. After extracting gray level co-occurrence matrix and completed local binary pattern features as spatial domain features and a number of statistical features of 2-D wavelet packet transform sub-images and 2-D Gabor filter banks transformed images as transform domain features, particle swarm optimization algorithm is used to select dominant features of the parallel and serial fused feature spaces. Classification is performed in two steps: First, focal livers are classified from the diffused ones and second, normal livers are distinguished from the fatty ones. For the used database, the maximum classification accuracy of 100% and 98.86% is achieved by serial and parallel feature fusion modes, respectively, using leave-one-out cross validation (LOOCV) method and support vector machine (SVM) classifier.

7

Wavelet Packet Transform based Speech Enhancement via Two-Dimensional SPP Estimator with Generalized Gamma Priors

Sun P., Qin J.

Archives of Acoustics

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2016

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Vol. 41, No. 3

579--590

EN

Despite various speech enhancement techniques have been developed for different applications, existing methods are limited in noisy environments with high ambient noise levels. Speech presence probability (SPP) estimation is a speech enhancement technique to reduce speech distortions, especially in low signalto-noise ratios (SNRs) scenario. In this paper, we propose a new two-dimensional (2D) Teager-energyoperators (TEOs) improved SPP estimator for speech enhancement in time-frequency (T-F) domain. Wavelet packet transform (WPT) as a multiband decomposition technique is used to concentrate the energy distribution of speech components. A minimum mean-square error (MMSE) estimator is obtained based on the generalized gamma distribution speech model in WPT domain. In addition, the speech samples corrupted by environment and occupational noises (i.e., machine shop, factory and station) at different input SNRs are used to validate the proposed algorithm. Results suggest that the proposed method achieves a significant enhancement on perceptual quality, compared with four conventional speech enhancement algorithms (i.e., MMSE-84, MMSE-04, Wiener-96, and BTW).

8

Wykorzystanie analizy WPT i sieci neuronowych PNN w diagnozowaniu zakłóceń w dopływie paliwa do cylindrów

Czech P., Madej H.

Problemy Eksploatacji

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2009

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nr 1

17-26

PL

W artykule przedstawiono wyniki badań diagnostycznych silnika spalinowego przy zastosowaniu pakietu analizy falkowej (WPT) i probabilistycznej sieci neuronowej. Obiektem badań był czterocylindrowy silnik spalinowy z zapłonem iskrowym. Głównym celem badań było określenie wpływu symulowanego braku dopływu paliwa do poszczególnych cylindrów na sygnał przyspieszeń drgań kadłuba silnika. Zarejestrowane sygnały przyspieszeń drgań zostały poddane analizie za pomocą WPT w celu określenia entropii sygnału na poszczególnych poziomach dekompozycji. Określona wartość entropii stanowiła podstawę do budowy wzorców stanów pracy silnika przeznaczonych do uczenia sieci neuronowych. Z przeprowadzonych badań wynika, że istnieje możliwość wykorzystania analizy WPT i probabilistycznych sztucznych sieci neuronowych do diagnozowania uszkodzeń silników spalinowych.

EN

An investigation of a fault diagnostic technique for internal combustion engine using wavelet packet transform (WPT) and probabilistic neural network is presented in this paper. The object of research was a four-cylinder spark ignition engine. The main purpose of the research was to determine the effect of the lack of fuel inflow to an individual cylinder of the engine block vibration signal. The vibration signals are decomposed by WPT to obtain the approximated and detailed coefficient and to calculate wavelet packet node entropy. The value of entropy was used as a basis in the construction of the states of engine operation intended for teaching probabilistic neural network. The experimental results indicated that the proposed system using the engine block vibration signal is effective and can be used for fault detection of an IC engine.

9

Matching pursuit algorithm with frame-based auditory optimized WP-dictionary for audio transient modeling

Petrovsky A., Petrovsky A.

Elektronika : konstrukcje, technologie, zastosowania

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2008

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Vol. 49, nr 4

74-79

EN

This paper presents the method of matching pursuit (MP) with frame based psychoacoustic optimized wavelet packet (WP) dictionary for selecting most relevant components to be used in compact representation transient part of signal. The wavelet dictionary for matching pursuit is composed of functions that are bounded by frame based psychoacoustic adaptive wavelet packet. Psychoacoustic motivated entropy based cost functions allow us to minimize perceptual relevance and adapt wavelet packet structure with reducing dictionary size. The proposed methodology for selecting most relevant components is based on maximizing the matching between the auditory excitation scalograms associated with original and modeled signal correspondingly. This technique allows to significant reduce the number of MP atoms in compare with well known techniques based on damped sinusoids and over-complete WP -dictionary.

PL

Artykuł prezentuje algorytm poszukiwania dopasowującego (ang. Matching Persuit) z percepcyjnie zoptymalizowanym słownikiem opartym na pakietowej transformacji falkowej. Algorytm zastosowano do jak najbardziej trafnego wyboru komponentów użytych do zwartego przedstawienia przejść w sygnale audio. Poszukiwanie dopasowujące wykorzystuje słownik złożony z funkcji określonych pakietową transformacją falkową, która percepcyjnie adaptuje się do ramek sygnału. Oparta na entropii i ocenie psychoakustycznej funkcja kosztu pozwala zminimalizować zależności percepcyjne i przystosować pakietową transformację falkową do ograniczonego rozmiaru słownika. Zaproponowana metodologia wyboru najbardziej istotnych komponentów opiera się na maksymalizacji dopasowania pomiędzy skalogramami percepcyjnego wzbudzenia skojarzonymi odpowiednio z sygnałem oryginalnym i modelowanym. Technika ta pozwala istotnie zredukować liczbę funkcji atomowych poszukiwania dopasowującego w stosunku do znanych technik, które wykorzystują tłumione sinusoidy i nadkompletne słowniki pakietowej transformacji falkowej.

10

A multiresolution auditory model using adaptive WP excitation scalograms

Petrovsky A.

Elektronika : konstrukcje, technologie, zastosowania

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2008

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Vol. 49, nr 4

65-70

EN

This paper proposes a multiresolution model of auditory excitation scalogram and applies it to the problem of audio signal transform and coding techniques. The model uses wavelet packet (WP) transform for time-frequency decomposition of the input signal. The WP tree structure selection is based on an optimality criterion formulated to minimize entropy based cost functions. From applications' point of view, multiresolution model of the auditory excitation scalogram based on adaptive to the signal WP is used to achieve Iow bit rate coding of digital audio signals with minimum perceived loss of quality in reconstructed signal.

PL

Artykuł przedstawia wielorozdzielczy model skalogramu wzbudzenia audytoryjnego oraz jego zastosowanie do przekształcenia i kodowania sygnału audio. Model wykorzystuje pakietową transformację falkową do dekompozycji czasowo-częstotliwościowej sygnału wejściowego. Struktura drzewiasta pakietowej transformacji falkowej jest dobierana na podstawie kryterium optymalności sformułowanego pod kątem minimalizacji entropii. Z punktu widzenia aplikacji, wielorozdzielczy model skalogramu wzbudzenia audytoryjnego, oparty na transformacie adaptującej się do sygnału, pozwala na uzyskanie niskiego tempa bitowego w kodowaniu audio przy minimalnej stracie jakości sygnału zrekonstruowanego.

11

Wavelet-based modeling of singular values for image texture classification

Ramakrishnan S., Selvan S.

Machine Graphics and Vision

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2006

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Vol. 15, No. 2

211-225

EN

A new algorithm based on the wavelet packet transform is proposed for the classification of image textures. Energy matrices are formed from subband coefficients of the wavelet packet transform. Singular value decomposition is then employed on the energy matrices. The probability density function of singular values is modeled as exponential distribution, and the model parameter is estimated using the maximum likelihood estimation technique. The model parameter, one for each subband, is used to form the feature vector. Classification is carried out using the Kullback-Leibler Distance (KLD). Performance of the algorithm is compared with model-based and feature-based methods in terms of the signal-to-noise ratio and the classification rate. Experimental results prove that the proposed algorithm achieves better classification rate under noisy environment.

12

Computing of masking thresholds for audio coders based on a quaternionic 4-band wavelet packet transform

Parfieniuk M., Baszun J., Petrovsky A. A.

Archives of Acoustics

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2006

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Vol. 31, No. 2

155-165

EN

A wavelet packet based on 4-band building blocks was used to implement an auditory model for 44.1kHz and 16kHz sampling frequency. The underlying paraunitary filter bank is implemented using a quaternionic lattice structurally insensitive to the quantization of its coefficients. Both the linear phase and orthogonality are possible for 4-band wavelets, so a better perceptual quality can be expected and an increased compression ratio for the coders based on the proposed solution in comparison to standard 2-band wavelet packets or a warped DFT transform. These features and a low computational complexity predestinate this approach to be a tempting alternative to widely known solutions.