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.

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.

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.

scholarly journals Power Quality Disturbance Recognition Using Empirical Wavelet Transform and Feature Selection

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 174
Author(s):  
Sihan Chen ◽  
Ziche Li ◽  
Guobing Pan ◽  
Fang Xu
Keyword(s):  
Feature Selection ◽  
Wavelet Transform ◽  
Power Quality ◽  
Electrical Equipment ◽  
Support Vector ◽  
Analysis Tool ◽  
Electrical Systems ◽  
Empirical Wavelet Transform ◽  
Power Quality Disturbance ◽  
System Calculation

With the growth of nonlinear electrical equipment, power quality disturbances (PQDs) often appear in electrical systems. To solve this, a practical heuristic methodology for PQD detection and classification based on empirical wavelet transform has been proposed. By using a multiresolution analysis tool, empirical wavelet transform, the voltage waveform signal is decomposed into several sub-signals, and some potential features are extracted in the statistical method. To reduce the feature vector dimensions, the ReliefF algorithm is used for feature selection and optimized for dimensionality reduction, which reduces the complexity of system calculation while ensuring accuracy. Finally, a classifier based on support vector machines (SVM) was built, and with the ranked feature vectors’ input, the PQD can be recognized. The experimental results verify that the classification results achieved high accuracy, which confirms the properties and robustness of the proposed approach in noisy environments.

A New Method EWT-Based for Rolling Element Bearing Weak Fault Diagnosis

2015 ◽  
Author(s):  
Xingxing Jiang ◽  
Shunming Li ◽  
Chun Cheng ◽  
Aijuan Li
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Scale Space ◽  
New Method ◽  
Rolling Element Bearing ◽  
Space Representation ◽  
Empirical Wavelet Transform ◽  
Rolling Element ◽  
Otsu’S Method ◽  
Weak Fault

The collected vibration signals from defective rolling element bearings are generally non-stationary and corrupted by strong background noise. The weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring. A new method EWT-based (Empirical Wavelet Transform) for bearing fault diagnosis is proposed in this paper. It consists of four parts. Firstly, the frequency ranges of meaningful modes are self-adaptively obtained by combining scale-space representation and Otsu’s method. Secondly, the meaningful modes are acquired by utilizing EWT to decompose the raw vibration signal. Thirdly, the first two modes possessing maximum kurtosis are selected as fault components. Lastly, the fault-related features could be observed in the time domain and envelope spectra of the selected modes. Experimental results verify that the proposed method is very effective for bearing weak fault diagnosis and the performance of proposed method is obviously better than the method of empirical mode decomposition (EMD).

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.

scholarly journals Quaternion Wavelet Analysis and Application in Image Denoising

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Ming Yin ◽  
Wei Liu ◽  
Jun Shui ◽  
Jiangmin Wu
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Basis Function ◽  
Signal To Noise Ratio ◽  
Scale Space ◽  
Analysis Tool ◽  
Magnitude Distribution ◽  
Wavelet Space ◽  
Quaternion Wavelet Transform ◽  
Gauss Distribution

The quaternion wavelet transform is a new multiscale analysis tool. Firstly, this paper studies the standard orthogonal basis of scale space and wavelet space of quaternion wavelet transform in spatialL2(R2), proves and presents quaternion wavelet’s scale basis function and wavelet basis function concepts in spatial scale spaceL2(R2;H), and studies quaternion wavelet transform structure. Finally, the quaternion wavelet transform is applied to image denoising, and generalized Gauss distribution is used to model QWT coefficients’ magnitude distribution, under the Bayesian theory framework, to recover the original coefficients from the noisy wavelet coefficients, and so as to achieve the aim of denoising. Experimental results show that our method is not only better than many of the current denoising methods in the peak signal to noise ratio (PSNR), but also obtained better visual effect.

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

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