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    Tytuł:
    Wykrywanie luzu w układzie tłok-cylinder przy wykorzystaniu analizy EMD
    Detection of clearance in the piston-cylinder assembly using EMD analysis
    Autorzy:
    Czech, P.
    Madej, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/258109.pdf
    Data publikacji:
    2008
    Wydawca:
    Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy
    Tematy:
    diagnostyka
    silnik spalinowy
    analiza EMD
    diagnosis
    combustion engine
    EMD analysis
    empirical mode decomposition analysis
    Opis:
    W artykule przedstawiono próbę oceny zużycia złożenia tłok-cylinder za pomocą sygnału drgań rejestrowanego na kadłubie silnika ZI. Obiektem badań był czterocylindrowy silnik spalinowy o pojemności 1,2 dm3. W badaniach zastosowano metodę empirycznej dekompozycji jako nowe podejście do diagnozowania uszkodzeń silników spalinowych. Poprzez zastosowanie tej metody w przeprowadzonych badaniach złożony sygnał drgań bloku silnika został rozłożony na szereg poziomów dekompozycji, umożliwiających rozróżnienie lokalnych właściwości sygnału w dziedzinie czasu. Uzyskane sygnały z procesu dekompozycji poddano analizie widmowej, w celu określenia ich cech energetycznych. Analizę przeprowadzono dla różnych pasm częstotliwości. Z przeprowadzonych badań wynika, że istnieje możliwość wykorzystania analizy EMD do oceny luzu w układzie tłok-cylinder.
    The paper presents an attempt to evaluate the wear of a piston-cylinder assembly with the aid of vibration signal recorded on the spark ignition (SI) engine body. The subject of the study was a four-cylinder combustion engine 1.2 dm3. In this research, an empirical mode decomposition (EMD) based approach for internal combustion engine fault diagnosis is investigated. EMD is a new time-frequency methods for analysing nonlinear and nonstationary signals generated by an IC engine. By using this method, the complicated vibration signal of the engine block was decomposed into an intrinsic mode function. It allowed us to differentiate local features of the vibration signal in time domain. Obtained signals were given under the analysis of the frequency domain to determine their energetic features. Analysis was conducted for various frequency bands. According to our studies, it is possible to utilise empirical mode decomposition (EMD) for the evaluation of the clearance in a piston-cylinder assembly.
    Źródło:
    Problemy Eksploatacji; 2008, 4; 65-72
    1232-9312
    Pojawia się w:
    Problemy Eksploatacji
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using Empirical Mode Decomposition of Backscattered Ultrasound Signal Power Spectrum for Assessment of Tissue Compression
    Autorzy:
    Byra, M.
    Wójcik, J.
    Nowicki, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/177950.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    tissue characterization
    tissue compression
    quantitative ultrasound
    empirical mode decomposition
    signal analysis
    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.
    Źródło:
    Archives of Acoustics; 2018, 43, 3; 447-453
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Investigation of supra-harmonics through signal processing methods in smart grids
    Autorzy:
    Yalcin, T.
    Ozdemir, M.
    Kostyla, P.
    Leonowicz, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/136245.pdf
    Data publikacji:
    2017
    Wydawca:
    EEEIC International Barbara Leonowicz Szabłowska
    Tematy:
    ensemble empirical mode decomposition
    power quality
    Fourier analysis
    Short Time Fourier Transformation
    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.
    Źródło:
    Transactions on Environment and Electrical Engineering; 2017, 2, 2; 80-85
    2450-5730
    Pojawia się w:
    Transactions on Environment and Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Some characteristic wave energy dissipation patterns along the Polish coast
    Autorzy:
    Rozynski, G.
    Szmytkiewicz, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/49190.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Instytut Oceanologii PAN
    Tematy:
    energy dissipation
    wave energy
    statistical parameter
    empirical mode decomposition
    singular spectrum analysis
    Polish coast
    Źródło:
    Oceanologia; 2018, 60, 4
    0078-3234
    Pojawia się w:
    Oceanologia
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Tests of basic voice stress detection techniques
    Autorzy:
    Staroniewicz, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/128166.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Poznańska. Instytut Mechaniki Stosowanej
    Tematy:
    Voice Stress Analysis
    Empirical Mode Decomposition
    analiza napięcia głosowego
    VSA
    empiryczna dekompozycja sygnału
    EMD
    Opis:
    The modern speech processing techniques enable new possibilities of potential applications. Besides speech and speaker recognition, also the information about speakers’ physical condition, emotional state or stress can be detected in speech signal. Since emotional stress can occur during deception, its detection in speech could be used for law or security services. The paper presents the comparative tests of two voice stress detection techniques: one based on trials of microtremors detection relying on an iterative EMD method (Empirical Mode Decomposition) and the second one based on the statistical analysis of fundamental frequency and MFCC parameters. The preliminary tests were carried on the group of 12 speakers (6 males and 6 females) answering yes/no to the list of a few dozen personal questions. The presented research revealed the speakers’ very high personal influence on the obtained results.
    Źródło:
    Vibrations in Physical Systems; 2019, 30, 1; 1-6
    0860-6897
    Pojawia się w:
    Vibrations in Physical Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions
    Autorzy:
    Zheng, Guoxiao
    Sun, Weifang
    Zhang, Hao
    Zhou, Yuqing
    Gao, Chen
    Powiązania:
    https://bibliotekanauki.pl/articles/2038054.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    tool wear condition monitoring
    empirical mode decomposition
    variational mode decomposition
    fourier synchro squeezed transform
    neighborhood component analysis
    long short-term memory network
    Opis:
    Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 4; 612-618
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Pulsation signals analysis of turbocharger turbine blades based on optimal EEMD and TEO
    Autorzy:
    Wang, Fengli
    Powiązania:
    https://bibliotekanauki.pl/articles/259800.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    urbocharger turbine blades
    pulsation signals analysis
    ensemble empirical mode decomposition
    Teager energy operator
    correlation kurtosis
    Opis:
    Turbocharger turbine blades suffer from periodic vibration and flow induced excitation. The blade vibration signal is a typical non-stationary and sometimes nonlinear signal that is often encountered in turbomachinery research and development. An example of such signal is the pulsating pressure and strain signals measured during engine ramp to find the maximum resonance strain or during engine transient mode in applications. As the pulsation signals can come from different disturbance sources, detecting the weak useful signals under a noise background can be difficult. For this type of signals, a novel method based on optimal parameters of Ensemble Empirical Mode Decomposition (EEMD) and Teager Energy Operator (TEO) is proposed. First, an optimization method was designed for adaptive determining appropriate EEMD parameters for the measured vibration signal, so that the significant feature components can be extracted from the pulsating signals. Then Correlation Kurtosis (CK) is employed to select the sensitive Intrinsic Mode Functions (IMFs). In the end, TEO algorithm is applied to the selected sensitive IMF to identify the characteristic frequencies. A case of measured sound signal and strain signal from a turbocharger turbine blade was studied to demonstrate the capabilities of the proposed method.
    Źródło:
    Polish Maritime Research; 2019, 3; 78-86
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of multiband filtering, empirical mode decomposition and short-time fourier transform used to extract physiological components from long-term heart rate variability
    Autorzy:
    Adamczyk, Krzysztof
    Polak, Adam G.
    Powiązania:
    https://bibliotekanauki.pl/articles/2052173.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    heart rate variability
    nonstationary signal analysis
    multiband filtering
    empirical mode decomposition
    short-time Fourier transform
    Hilbert transform
    Opis:
    Heart rate is constantly changing under the influence of many control signals, as manifested by heart rate variability (HRV). HRV is a nonstationary, irregularly sampled signal, the spectrum of which reveals distinct bands of high, low, very low and ultra-low frequencies (HF, LF, VLF, ULF). VLF and ULF components are the least understood, and their analysis requires HRV records lasting many hours. Moreover, there are still no well-established methods for the reliable extraction of these components. The aim of this work was to select, implement and compare methods which can solve this problem. The performance of multiband filtering (MBF), empirical mode decomposition and the short-time Fourier transform was tested, using synthetic HRV as the ground truth for methods evaluation as well as real data of three patients selected from 25 polysomnographic records with a clear HF component in their spectrograms. The study provided new insights into the components of long-term HRV, including the character of its amplitude and frequency modulation obtained with the Hilbert transform. In addition, the reliability of the extracted HF, LF, VLF and ULF waveforms was demonstrated, and MBF turned out to be the most accurate method, though the signal is strongly nonstationary. The possibility of isolating such waveforms is of great importance both in physiology and pathophysiology, as well as in the automation of medical diagnostics based on HRV.
    Źródło:
    Metrology and Measurement Systems; 2021, 28, 4; 643-660
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    EMD-based time-frequency analysis methods of non-stationary audio signals
    Autorzy:
    Lewandowski, Marcin
    Grodzicka, Salomea
    Powiązania:
    https://bibliotekanauki.pl/articles/2202413.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Poznańska. Instytut Mechaniki Stosowanej
    Tematy:
    empirical mode decomposition
    non-stationary audio data
    time-frequency analysis
    empiryczna metoda dekompozycji
    niestacjonarne dane dźwiękowe
    analiza czasowo-częstotliwościowa
    Opis:
    To ensure that any time series data is appropriately interpreted, it should be analyzed with proper signal processing tools. The most common analysis methods are kernel-based transforms, which use base functions and their modifications to represent time series data. This work discusses an analysis of audio data and two of those transforms - the Fourier transform and the wavelet transform based on a priori assumptions about the signal's linearity and stationarity. In audio engineering, these assumptions are invalid because the statistical parameters of most audio signals change with time and cannot be treated as an output of the LTI system. That is why recent approaches involve decomposition of a signal into different modes in a data-dependent and adaptive way, which may provide advantages over kernel-based transforms. Examples of such methods include empirical mode decomposition (EMD), ensemble EMD (EEMD), variational mode decomposition (VMD), or singular spectrum analysis (SSA). Simulations were performed with speech signal for kernel-based and data-dependent decomposition methods, which revealed that evaluated decomposition methods are promising approaches to analyzing non-stationary audio data.
    Źródło:
    Vibrations in Physical Systems; 2022, 33, 2; art. no. 2022215
    0860-6897
    Pojawia się w:
    Vibrations in Physical Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Dynamical complexity of human responses: a multivariate data-adaptive framework
    Autorzy:
    Ahmed, M.
    Rehman, N.
    Looney, D.
    Rutkowski, T.
    Mandic, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/201559.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    multivariate sample entropy
    multivariate empirical mode decomposition (MEMD)
    multivariate multiscale entropy
    complexity analysis
    multivariate complexity
    postural sway analysis
    stride interval analysis
    brain consciousness analysis
    alpha-attenuated EEG data
    Opis:
    Established complexity measures typically operate at a single scale and thus fail to quantify inherent long-range correlations in real-world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) method has the ability to detect fractal correlations and has been used successfully to assess the complexity of univariate data. However, multivariate observations are common in many real-world scenarios and a simultaneous analysis of their structural complexity is a prerequisite for the understanding of the underlying signal-generating mechanism. For this purpose, based on the notion of multivariate sample entropy, the standard MSE method is extended to the multivariate case, whereby for rigor, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows us to gain better understanding of the complexity of the underlying multivariate real-world process, together with more degrees of freedom and physical interpretation in the analysis. Simulations on both synthetic and real-world biological multivariate data sets support the analysis.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 433-445
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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