Temat: Empirical Mode Decomposition

Skocz do pozycji: 1.

Tytuł:: A window based method to reduce the end-effect in Empirical Mode Decomposition
Autorzy:: Cotogno, M.
Cocconcelli, M.
Rubini, R.
Tematy:: empirical mode decomposition
intrinsic mode function
end-effect problem
windowing; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Powiązania:: https://bibliotekanauki.pl/articles/328237.pdf Link otwiera się w nowym oknie
Opis:: Empirical Mode Decomposition technique (EMD) is a recent development in non-stationary and non-linear data analysis. It is an algorithm which adaptively decomposes the signal in the sum of Intrinsic Mode Functions (IMFs) from which the instantaneous frequency can be easily computed. EMD has proven its effectiveness but is still affected from various problems. One of these is the “end-effect”, a phenomenon occurring at the start and at the end of the data due to the splines fitting on which the EMD is based. Various techniques have been tried to overcome the end-effect, like different data extension or mirroring procedures at the data boundary. In this paper we made use of the IMFs orthogonality property to apply a symmetrical window to the data before EMD for end-effect reduction. Subsequently the IMFs are post-processed to compensate for data alteration due to windowing. The simulations show that IMFs obtained with this method are of better quality near the data boundaries while remaining almost identical to classical EMD ones.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: A hybrid denoising method for gyroscopes based on multiple screening
Autorzy:: Rong, Hailong
Jin, Tianlei
Wang, Hao
Wu, Xiaohui
Zou, Ling
Tematy:: micro electromechanical system
multiple screening mechanism
empirical mode decomposition; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Powiązania:: https://bibliotekanauki.pl/articles/58973595.pdf Link otwiera się w nowym oknie
Opis:: To reduce the random error of microelectromechanical system (MEMS) gyroscope, a hybrid method combining improved empirical mode decomposition (EMD) and least squares algorithm (LS) is proposed. Firstly, based on the multiple screening mechanism, intrinsic mode functions (IMFs) from the first decomposition are divided into noise IMFs, strong noise mixed IMFs, weak noise mixed IMFs and signal IMFs. Secondly, according to their characteristics, they are processed again. IMFs from the second decomposition are divided into noise IMFs and signal IMFs. Finally, useful signal is gathered to obtain the final denoising signal. Compared with some other denoising methods proposed in recent years, the experimental results show that the proposed method has obvious advantages in suppressing random error, greatly improving the signal quality and improving the accuracy of inertial navigation.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: EMD-based time-frequency analysis methods of audio signals
Autorzy:: Lewandowski, Marcin
Deng, Qizhang
Tematy:: empirical mode decomposition
non-stationary audio data
time-frequency analysis; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Powiązania:: https://bibliotekanauki.pl/articles/58973071.pdf Link otwiera się w nowym oknie
Opis:: Using appropriate signal processing tools to analyze time series data accurately is essential for correctly interpreting the underlying processes. Commonly employed methods include kernel-based transforms that utilize base functions and modifications to depict time series data. This paper refers to the analysis of audio data using two such transforms: the Fourier transform and the wavelet transform, both based on assumptions regarding the signal's linearity and stationarity. However, in audio engineering, these assumptions often do not hold as the statistical characteristics of most audio signals vary over time, making them unsuitable for treatment as outputs from a Linear Time-Invariant (LTI) system. Consequently, more recent methods have shifted towards breaking down signals into various modes in an adaptive, data-specific manner, potentially offering benefits over traditional kernel-based methods. Techniques like empirical mode decomposition and Holo-Hilbert Spectral Analysis are examples of this. The effectiveness of these methods was tested through simulations using speech signals for both kernel-based and adaptive decomposition methods, demonstrating that these adaptive methods are effective for analyzing audio data that is both nonstationary and an output of the nonlinear system.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Using Empirical Mode Decomposition of Backscattered Ultrasound Signal Power Spectrum for Assessment of Tissue Compression
Autorzy:: Byra, M.
Wójcik, J.
Nowicki, A.
Tematy:: tissue characterization
tissue compression
quantitative ultrasound
empirical mode decomposition
signal analysis; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/177950.pdf Link otwiera się w nowym oknie
Opis:: Quantitative ultrasound has been widely used for tissue characterization. In this paper we propose a new approach for tissue compression assessment. The proposed method employs the relation between the tissue scatterers’ local spatial distribution and the resulting frequency power spectrum of the backscattered ultrasonic signal. We show that due to spatial distribution of the scatterers, the power spectrum exhibits characteristic variations. These variations can be extracted using the empirical mode decomposition and analyzed. Validation of our approach is performed by simulations and in-vitro experiments using a tissue sample under compression. The scatterers in the compressed tissue sample approach each other and consequently, the power spectrum of the backscattered signal is modified. We present how to assess this phenomenon with our method. The proposed in this paper approach is general and may provide useful information on tissue scattering properties.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Taking advantage of empirical mode decomposition in diagnosing gear faults
Wykorzystanie empirycznej dekompozycji sygnału w diagnostyce uszkodzeń przekładni zębatych
Autorzy:: Łazarz, B.
Madej, H.
Czech, P.
Tematy:: diagnostyka
przekładnia zębata
empiryczna dekompozycja sygnału
diagnostics
gear
empirical mode decomposition; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Powiązania:: https://bibliotekanauki.pl/articles/328818.pdf Link otwiera się w nowym oknie
Opis:: The study presents the application of empirical mode decomposition as a tool useful in diagnosing faults in gears. The method is a modern algorithm used for non-linear and non-stationary signals. Using this algorithm, it is possible to decompose a signal into a finite sum of component called intrinsic mode functions (IMF). For each IMF, the number of extremes and the number of transitions through zero is equal or different, by maximum one, and the mean value of envelope determined by the signal extremes equals zero. In practice, natural signals do not meet these conditions. In the experiment, a gearbox operating in a circulating power system was used, with 16 and 24 pinion and wheel teeth, respectively. The measurements were carried out for a non-damaged gear and for a gear with a modelled fault, operating at various rotational speeds and under different loads.
W opracowaniu przedstawiono zastosowanie empirycznej dekompozycji sygnału jako narzędzia przydatnego w diagnostyce uszkodzeń przekładni zębatych. Metoda ta jest nowoczesnym algorytmem stosowanym dla sygnałów nieliniowych i niestacjonarnych. Wykorzystując ten algorytm można rozłożyć sygnał na skończoną sumę składowych zwanych funkcjami wewnętrznymi (IMF). Dla każdego IMF liczba ekstremów i liczba przejść przez zero jest równa bądź różna o maksimum jeden, a wartość średnia obwiedni określonej przez ekstrema sygnału równa się zero. W praktyce naturalne sygnały nie spełniają tych warunków. W eksperymencie wykorzystano przekładnie zębatą pracującą w układzie mocy krążącej o licznie zębów zębnika i koła odpowiednio 16 i 24. Pomiary przeprowadzono dla przekładni nieuszkodzonej oraz z zamodelowanym uszkodzeniem, pracującej przy różnych prędkościach obrotowych i różnych obciążeniach.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Investigation of supra-harmonics through signal processing methods in smart grids
Autorzy:: Yalcin, T.
Ozdemir, M.
Kostyla, P.
Leonowicz, Z.
Tematy:: ensemble empirical mode decomposition
power quality
Fourier analysis
Short Time Fourier Transformation; Pokaż więcej
Wydawca:: EEEIC International Barbara Leonowicz Szabłowska
Powiązania:: https://bibliotekanauki.pl/articles/136245.pdf Link otwiera się w nowym oknie
Opis:: Nowadays supra-harmonic distortion studies are gaining attention day by day in power quality research area. When handling communication systems especially Power Line Carrier (PLC) systems in frequency range 2-150 kHz, they are suitable for causing electromagnetic interference (EMI) to other systems. This study shows results of analysis employing advanced method called ensemble empirical mode decomposition (EEMD) to describe supra-harmonic distortion. Unlike the traditional method (short time fourier transform-STFT), EEMD gives extensive representation for supra-harmonic components.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes
Autorzy:: Sun, Shuang
Przystupa, Krzysztof
Wei, Ming
Yu, Han
Ye, Zhiwei
Kochan, Orest
Tematy:: malfunction diagnostics
naive Bayes
moth-flame optimization algorithm
ensemble empirical mode decomposition; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Powiązania:: https://bibliotekanauki.pl/articles/1841936.pdf Link otwiera się w nowym oknie
Opis:: Fault diagnosis is part of the maintenance system, which can reduce maintenance costs, increase productivity, and ensure the reliability of the machine system. In the fault diagnosis system, the analysis and extraction of fault signal characteristics are very important, which directly affects the accuracy of fault diagnosis. In the paper, a fast bearing fault diagnosis method based on the ensemble empirical mode decomposition (EEMD), the moth-flame optimization algorithm based on Lévy flight (LMFO) and the naive Bayes (NB) is proposed, which combines traditional pattern recognition methods meta-heuristic search can overcome the difficulty of selecting classifier parameters while solving small sample classification under reasonable time cost. The article uses a typical rolling bearing system to test the actual performance of the method. Meanwhile, in comparison with the known algorithms and methods was also displayed in detail. The results manifest the efficiency and accuracy of signal sparse representation and fault type classification has been enhanced.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Speech Enhancement Using Sliding Window Empirical Mode Decomposition and Hurst-based Technique
Autorzy:: Poovarasan, Selvaraj
Chandra, Eswaran
Tematy:: speech enhancement
Empirical Mode Decomposition
EMD
Intrinsic Mode Functions
hurst exponent
Sliding Window
SW; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Powiązania:: https://bibliotekanauki.pl/articles/176311.pdf Link otwiera się w nowym oknie
Opis:: The most challenging in speech enhancement technique is tracking non-stationary noises for long speech segments and low Signal-to-Noise Ratio (SNR). Different speech enhancement techniques have been proposed but, those techniques were inaccurate in tracking highly non-stationary noises. As a result, Empirical Mode Decomposition and Hurst-based (EMDH) approach is proposed to enhance the signals corrupted by non-stationary acoustic noises. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by considering the least corrupted IMF. Though it increases SNR, the time and resource consumption were high. Also, it requires a significant improvement under nonstationary noise scenario. Hence, in this article, EMDH approach is enhanced by using Sliding Window (SW) technique. In this SWEMDH approach, the computation of EMD is performed based on the small and sliding window along with the time axis. The sliding window depends on the signal frequency band. The possible discontinuities in IMF between windows are prevented by the total number of modes and the number of sifting iterations that should be set a priori. For each module, the number of lifting iterations is determined by decomposition of many signal windows by standard algorithm and calculating the average number of sifting steps for each module. Based on this approach, the time complexity is reduced significantly with suitable quality of decomposition. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Employing empirical mode decomposition to determine solar radiation intensity curve
Zastosowanie empirycznej dekompozycji modów do wyznaczania krzywej natężenia promieniowania słonecznego
Autorzy:: Kapica, J.
Scibisz, M.
Tematy:: empirical mode decomposition
signal filtration
solar radiation
solar energy
weather condition
insolation
LabView programming; Pokaż więcej
Wydawca:: Komisja Motoryzacji i Energetyki Rolnictwa
Powiązania:: https://bibliotekanauki.pl/articles/792710.pdf Link otwiera się w nowym oknie
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Wasserstein Distance-EEMD Enhanced Multi-Head Graph Attention Network for Rolling Bearing Fault Diagnosis Under Different Working Conditions
Autorzy:: Wang, Xingbing
Yao, Yunfeng
Gao, Chen
Tematy:: ensemble empirical mode decomposition
Wasserstein distance
multi head graph attention Network
fault diagnosis
rolling bearing; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Powiązania:: https://bibliotekanauki.pl/articles/59112791.pdf Link otwiera się w nowym oknie
Opis:: Traditional fault diagnosis models often overlook the interconnections between segments of vibration data, resulting in the loss of critical feature information. Additionally, the vibration signals of rolling bearings exhibit non-linear behaviors during operation. Therefore, an efficient fault diagnosis model tailored for rolling bearings is proposed in this paper. In the proposed model, the 1D vibration signals are first preprocessed using ensemble empirical mode decomposition (EEMD). This technique generates multiple intrinsic mode functions (IMF) as individual nodes. The percentage distance between each node is calculated using the Wasserstein distance (WD) to capture the relationships between nodes and use it as the edge weights to construct a node graph. This unique approach enhances the transformation of 1D vibration signals into a node graph representation, preserving important information. An improved multi-head graph attention network (MGAT) model is established to extract features and perform classification on the node graph. This MGAT model effectively utilizes the relationships between nodes and enhances the accuracy of fault diagnosis. The experimental results demonstrate that the proposed method achieves higher accuracy compared to similar modelswhile requiring less processing time. The proposed approach contributes significantly to the field of fault diagnosis for rolling bearings and provides a valuable tool for practical applications.
Dostawca treści:: Biblioteka Nauki

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