scholarly journals An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 135 ◽  
Author(s):  
Zezhong Feng ◽  
Jun Ma ◽  
Xiaodong Wang ◽  
Jiande Wu ◽  
Chengjiang Zhou
Keyword(s):  
Wavelet Transform ◽  
Resonant Frequency ◽  
Correlation Coefficient ◽  
Frequency Band ◽  
Scale Space ◽  
Center Frequency ◽  
Application Process ◽  
Histogram Method ◽  
Feature Extraction Method ◽  
Empirical Wavelet Transform

The Empirical Wavelet Transform (EWT), which has a reliable mathematical derivation process and can adaptively decompose signals, has been widely used in mechanical applications, EEG, seismic detection and other fields. However, the EWT still faces the problem of how to optimally divide the Fourier spectrum during the application process. When there is noise interference in the analyzed signal, the parameterless scale-space histogram method will divide the spectrum into a variety of narrow bands, which will weaken or even fail to extract the fault modulation information. To accurately determine the optimal resonant demodulation frequency band, this paper proposes a method for applying Adaptive Average Spectral Negentropy (AASN) to EWT analysis (AEWT): Firstly, the spectrum is segmented by the parameterless clustering scale-space histogram method to obtain the corresponding empirical mode. Then, by comprehensively considering the Average Spectral Negentropy (ASN) index and correlation coefficient index on each mode, the correlation coefficient is used to adjust the ASN value of each mode, and the IMF with the highest value is used as the center frequency band of the fault information. Finally, a new resonant frequency band is reconstructed for the envelope demodulation analysis. The experimental results of different background noise intensities show that the proposed method can effectively detect the repetitive transients in the signal.

Adaptive fault detection of the bearing in wind turbine generators using parameterless empirical wavelet transform and margin factor

2019 ◽  
Vol 25 (6) ◽  
pp. 1263-1278 ◽  
Author(s):  
Wei Teng ◽  
Wei Wang ◽  
Haixing Ma ◽  
Yibing Liu ◽  
Zhiyong Ma ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Wind Turbines ◽  
Vibration Signal ◽  
Scale Space ◽  
Operating Conditions ◽  
Turbine Generators ◽  
Detection Approach ◽  
Empirical Wavelet Transform ◽  
Fault Information

Wind turbines revolve in difficult operating conditions due to stochastic loads and produce massive vibration signals, which cause obstacles in detecting potential fault information. To overcome this, an adaptive fault detection approach is presented in this paper on the basis of parameterless empirical wavelet transform (PEWT) and the margin factor. PEWT can decompose the vibration signal into a series of empirical modes (EMs) through splitting its Fourier spectrum, using the scale space method and adaptively constructing an orthogonal wavelet filter bank. The margin factor is utilized as a key metric for automatically selecting the EM which is sensitive to the potential faults. The method presented in this paper will improve the efficiency and accuracy of fault information for the condition monitoring of wind turbines.

A Signal Processing of In-Cylinder Pressure for the Resonant Frequency Prediction of Combustion Process in Diesel Engines

2018 ◽  
Author(s):  
Ximing Chen ◽  
Long Liu ◽  
Jiguang Zhang ◽  
Jingtao Du
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Resonant Frequency ◽  
Diesel Engines ◽  
Combustion Process ◽  
Combustion Noise ◽  
Injection Timing ◽  
Cylinder Pressure ◽  
Empirical Wavelet Transform

The combustion resonance is a focal point of the analysis of combustion and thermodynamic processes in diesel engines, such as detecting ‘knock’ and predicting combustion noise. Combustion resonant frequency is also significant for the estimation of in-cylinder bulk gas temperature and trapped mass. Normally, the resonant frequency information is contained in in-cylinder pressure signals. Therefore, the in-cylinder pressure signal processing is used for resonant frequency calculation. Conventional spectral analyses, such as FFT (Fast Fourier transform), are unsuitable for processing in-cylinder pressure signals because of its non-stationary characteristic. Other approaches to deal with non-stationary signals are Short-Time Fourier Transform (STFT) and Continue Wavelet Transform (CWT). However, the choice of size and shape of window for STFT and the selection of wavelet basis for CWT are totally empirical, which is the limit for precisely calculating the resonant frequency. In this study, an approach based on Empirical Wavelet Transform (EWT) and Hilbert Transform (HT) is proposed to process in-cylinder pressure signals and extract resonant frequencies. In order to decompose in-cylinder pressure spectrum precisely, the EWT are applied for separating the frequency band corresponding combustion resonance mode from other irrelevant modes adaptively. The signals containing combustion resonant mode is processed by HT, so that the instantaneous resonant frequency and amplitude can be extracted. Validation is performed by four in-cylinder pressure signals with different injection timing. And the effects of injection timing on resonant frequency are discussed.

scholarly journals A Design Methodology for EV-WPT Systems to Resonate at Arbitrary Given Bands

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 213
Author(s):  
Yong Yin ◽  
Yuhua Xiao ◽  
Chengliang Wang ◽  
Qingsheng Yang ◽  
Yahui Jia ◽  
...  
Keyword(s):  
Resonant Frequency ◽  
Frequency Band ◽  
Resonant State ◽  
Design Method ◽  
Single Band ◽  
Frequency Deviation ◽  
Center Frequency ◽  
Current Response ◽  
Efficient Energy Transfer ◽  
The Given

Due to the effects of splitting frequency and cross coupling, the resonant frequency of the WPT system usually deviates from the given frequency band, and the system operating at the given frequency band suffers a very low output power. Ensuring that electric vehicle wireless power transfer (EV-WPT) systems operate at a resonant state is the prerequisite for efficient energy transfer. For this purpose, a novel design method by manipulating the eigenstate parameters is proposed in this paper. The proposed system can make a EV-WPT system with arbitrary coil successfully to resonate at any given bands, not just a single band. Therefore, the method designed in this article cannot only eliminate the problem of low power caused by frequency deviation, but also realize the application requirements of multiple frequency bands. Firstly, this article establishes an accurate state space model of an n-coil fully coupled EV-WPT system, and after that, the analytical current response on each circuit is derived. Based on that, a detailed frequency spectrum analysis is presented, along with several essential spectrum parameters’ derivations, including center frequencies and bandwidths. Then, with the center frequency and bandwidth as the design indexes, a novel methodology of designing to make EV-WPT systems achieve resonant-state at arbitrary given bands is derived. Finally, simulation and experimental verification are carried out. Simulation and experimental results show that whether it is a single-band or multi-band system, the accuracy of the value under designed resonant frequency is less than 0.01, which can effectively eliminate the frequency deviation phenomenon and obtain the maximum power output at the given frequency band.

scholarly journals A Rolling Bearing Fault Diagnosis-Optimized Scale-Space Representation for the Empirical Wavelet Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Zechao Liu ◽  
Jianming Ding ◽  
Jianhui Lin ◽  
Yan Huang
Keyword(s):  
Wavelet Transform ◽  
Rolling Bearing ◽  
Scale Space ◽  
Ensemble Empirical Mode Decomposition ◽  
Space Representation ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Engineering Practices ◽  
Fault Information

Rolling element bearings have been widely used in mechanical systems, such as electric motors, generators, pumps, gearboxes, railway axles, and turbines, etc. Therefore, the detection of rolling bearing faults has been a hot research topic in engineering practices. Envelope demodulation represents a fundamental method for extracting effective fault information from measured vibration signals. However, the performance of envelope demodulation depends heavily on the selection of the filter band and central frequencies. The empirical wavelet transform (EWT), a new signal decomposition method, provides a framework for arbitrarily segmenting the Fourier spectrum of an analysed signal. Scale-space representation (SSR) can adaptively detect the boundaries of the EWT; however, it has two shortcomings: slow calculation speeds and invalid boundary detection results. Accordingly, an EWT method based on optimized scale-space representation (OSSR), namely, the EWTOSSR, is proposed. The effectiveness of the EWTOSSR is verified by comparisons between the simulation and the experimental signals. The results show that the EWTOSSR can automatically and effectively segment the EWT spectrum to extract fault information. Compared with three well-known methods (the traditional EWT, ensemble empirical mode decomposition (EEMD), and the fast kurtogram), the EWTOSSR exhibits a much better fault detection performance.

scholarly journals Mono-Component Feature Extraction for Condition Assessment in Civil Structures Using Empirical Wavelet Transform

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4280 ◽  
Author(s):  
Xia ◽  
Zhou
Keyword(s):  
Wavelet Transform ◽  
Structural Characteristics ◽  
Scale Space ◽  
Structural Features ◽  
Modal Parameters ◽  
Space Representation ◽  
Complex Signals ◽  
Civil Structures ◽  
Clustering Problem ◽  
Empirical Wavelet Transform

This paper proposes a methodology to process and interpret the complex signals acquired from the health monitoring of civil structures via scale-space empirical wavelet transform (EWT). The FREEVIB method, a widely used instantaneous modal parameters identification method, determines the structural characteristics from the individual components separated by EWT first. The scale-space EWT turns the detecting of the frequency boundaries into the scale-space representation of the Fourier spectrum. As well, to find meaningful modes becomes a clustering problem on the length of minima scale-space curves. The Otsu’s algorithm is employed to determine the threshold for the clustering analysis. To retain the time-varying features, the EWT-extracted mono-components are analyzed by the FREEVIB method to obtain the instantaneous modal parameters and the linearity characteristics of the structures. Both simulated and real SHM signals from civil structures are used to validate the effectiveness of the present method. The results demonstrate that the proposed methodology is capable of separating the signal components, even those closely spaced ones in frequency domain, with high accuracy, and extracting the structural features reliably.

scholarly journals Feature Extraction Method for Hydraulic Pump Fault Signal Based on Improved Empirical Wavelet Transform

Processes ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 824 ◽  
Author(s):  
Zheng ◽  
Wang ◽  
Zhu ◽  
Tang ◽  
Wang
Keyword(s):  
Wavelet Transform ◽  
Power Spectrum ◽  
Amplitude Spectrum ◽  
Hydraulic Pump ◽  
Fourier Amplitude ◽  
Feature Extraction Method ◽  
Empirical Wavelet Transform ◽  
Novel Method ◽  
Feature Information ◽  
Fourier Amplitude Spectrum

There are many interference components in Fourier amplitude spectrum of a contaminated fault signal, and thus the segment obtained based on the spectrum can lead to serious over-decomposition of empirical wavelet transform (EWT). Aiming to resolve the above problems, a novel method named improved empirical wavelet transform (IEWT) is proposed. Because the power spectrum is less sensitive to the contaminated interference and manifests the presence of fault feature information, IEWT replaces the Fourier amplitude spectrum of EWT with power spectrum in segment acquirement, and threshold processing is also introduced to eliminate the bad influence on the acquirement, and thus the best decomposition result of IEWT can be obtained based on feature energy ratio (FER). The loose slipper fault signal of hydraulic pump is tested and verified. The result demonstrates that the proposed method is superior and can extract the fault feature information accurately.

scholarly journals New method for gear fault diagnosis using empirical wavelet transform, Hilbert transform, and cosine similarity metric

2020 ◽  
Vol 12 (6) ◽  
pp. 168781402092720
Author(s):  
Toufik Bettahar ◽  
Chemseddine Rahmoune ◽  
Djamel Benazzouz ◽  
Boualem Merainani
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Cosine Similarity ◽  
Similarity Metric ◽  
Feature Extraction Method ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
New Feature

In this article, a new feature extraction method is proposed for gear fault diagnosis by combining the empirical wavelet transform, Hilbert transform, and cosine similarity metric. In the first place, a number of empirical mode components acquisitions are done, using empirical wavelet transform. Since different empirical modes have different sensitivities to fault, not all of them are needed for further analysis. Therefore, the most sensitive empirical modes are selected using the cosine similarity metric method. Hilbert transform was then used to obtain the envelope for amplitude modulation. Finally, spectral analysis using fast Fourier transform is applied on the obtained envelope. Gear test rig with gears under different fault states has revealed an effective outcome and a solid stability of this new approach. The obtained results show that our approach is efficiently able to detect and expose the gear faults signatures, that is, it highlights their frequencies and the corresponding harmonics with respect to the rotary frequency. Furthermore, this proposed method demonstrates more satisfactory and advantageous performances compared to those of fast kurtogram, or the autogram.

scholarly journals The Empirical Watershed Wavelet

2020 ◽  
Vol 6 (12) ◽  
pp. 140
Author(s):  
Basile Hurat ◽  
Zariluz Alvarado ◽  
Jérôme Gilles
Keyword(s):  
Wavelet Transform ◽  
Scale Space ◽  
Space Representation ◽  
Analysis Tool ◽  
Image Deconvolution ◽  
Wavelet Filters ◽  
Whole Process ◽  
Multi Resolution Analysis ◽  
Empirical Wavelet Transform ◽  
Adaptive Transform

The empirical wavelet transform is an adaptive multi-resolution analysis tool based on the idea of building filters on a data-driven partition of the Fourier domain. However, existing 2D extensions are constrained by the shape of the detected partitioning. In this paper, we provide theoretical results that permits us to build 2D empirical wavelet filters based on an arbitrary partitioning of the frequency domain. We also propose an algorithm to detect such partitioning from an image spectrum by combining a scale-space representation to estimate the position of dominant harmonic modes and a watershed transform to find the boundaries of the different supports making the expected partition. This whole process allows us to define the empirical watershed wavelet transform. We illustrate the effectiveness and the advantages of such adaptive transform, first visually on toy images, and next on both unsupervised texture segmentation and image deconvolution applications.

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