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 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 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.

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 A parameterless scale-space approach to find meaningful modes in histograms — Application to image and spectrum segmentation

2014 ◽  
Vol 12 (06) ◽  
pp. 1450044 ◽  
Author(s):  
Jérôme Gilles ◽  
Kathryn Heal
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Scale Space ◽  
Space Representation ◽  
Grayscale Image ◽  
Local Minima ◽  
Clustering Problem ◽  
Space Curves ◽  
Space Approach

In this paper, we present an algorithm to automatically detect meaningful modes in a histogram. The proposed method is based on the behavior of local minima in a scale-space representation. We show that the detection of such meaningful modes is equivalent in a two classes clustering problem on the length of minima scale-space curves. The algorithm is easy to implement, fast and does not require any parameter. We present several results on histogram and spectrum segmentation, grayscale image segmentation and color image reduction.

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 Equiprobable Symbolization Pattern Entropy for Time Series Complexity Measurement

2021 ◽  
Author(s):  
Fuyi Wang ◽  
Leo Yu Zhang
Keyword(s):  
Time Series ◽  
Structural Characteristics ◽  
Sliding Window ◽  
Original Data ◽  
Structural Features ◽  
Complex Signals ◽  
Complexity Measurement ◽  
Conversion Frequency ◽  
Consistent Increase ◽  
Relationship Of

Abstract In order to more effectively mine the structural features in time series, while simplifying the complexity of time series analysis, equiprobable symbolization pattern entropy (EPSPE) based on time series symbolization combined with sliding window technology is proposed in this paper. Firstly, time series are implemented symbolic procession according to the equal probability distribution of the original data, which greatly simplifies the difficulty of analyzing the signal on the premise of small loss of precision to the original signal. Then, sliding window technique is used to obtain a finite number of different symbolic patterns, and the pattern pairs are determined by calculating the conversion between the symbolic patterns. Next, the conversion frequency between symbolized patterns is counted to calculate the probability of the pattern pairs, thus estimating the complexity measurement of complex signals. The results of test using the Logistic system with different parameters show that compared with multiscale sample entropy(MSE), EPSPE can more concisely and intuitively reflect the structural characteristics of time series. Finally, EPSPE is used to investigate the natural wind field signals collected at an outdoor space in which nine high precision two-dimensional (2D) ultrasonic anemometers are deployed in line with 1m interval. The values of EPSPE show consistent increase or decrease trend with the spatial regular arrangement of the nine anemometers. While the results of MSE are irregular, and cannot accurately predict the spatial deployment relationship of nine 2D ultrasonic anemometers.

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 Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

2021 ◽  
Vol 13 (17) ◽  
pp. 3375
Author(s):  
Zhen Fang ◽  
Jiayong Yu ◽  
Xiaolin Meng
Keyword(s):  
Wavelet Transform ◽  
Dynamic Response ◽  
Field Experiments ◽  
Suspension Bridge ◽  
Modal Parameters ◽  
Structural Dynamic ◽  
Response Data ◽  
Empirical Wavelet Transform ◽  
Bridge Structures ◽  
Gnss Data

It is difficult to accurately identify the dynamic deformation of bridges from Global Navigation Satellite System (GNSS) due to the influence of the multipath effect and random errors, etc. To solve this problem, an improved empirical wavelet transform (EWT)-based procedure was proposed to denoise GNSS data and identify the modal parameters of bridge structures. Firstly, the Yule–Walker algorithm-based auto-power spectrum and Fourier spectrum were jointly adopted to segment the frequency bands of structural dynamic response data. Secondly, the improved EWT algorithm was used to decompose and reconstruct the dynamic response data according to a correlation coefficient-based criterion. Finally, Natural Excitation Technique (NExT) and Hilbert Transform (HT) were applied to identify the modal parameters of structures from the decomposed efficient components. Two groups of simulation data were used to validate the feasibility and reliability of the proposed method, which consisted of the vibration responses of a four-storey steel frame model, and the acceleration response data of a suspension bridge. Moreover, field experiments were carried out on the Wilford suspension bridge in Nottingham, UK, with GNSS and an accelerometer. The fundamental frequency (1.6707 Hz), the damping ratio (0.82%), as well as the maximum dynamic displacements (10.10 mm) of the Wilford suspension bridge were detected by using this proposed method from the GNSS measurements, which were consistent with the accelerometer results. In conclusion, the analysis revealed that the improved EWT-based method was capable of accurately identifying the low-order, closely spaced modal parameters of bridge structures under operational conditions.

Sign in / Sign up

Export Citation Format

Share Document