Improved Tensor-Based Singular Spectrum Analysis Based on Single Channel Blind Source Separation Algorithm and Its Application to Fault Diagnosis

Yang, Dan; Yi, Cancan; Xu, Zengbin; Zhang, Yi; Ge, Mao; Liu, Changming

doi:10.3390/app7040418

Cited by 12 publications

(16 citation statements)

References 35 publications

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“…The wavelet threshold method is to transform the signal to the wavelet domain; then obtain the wavelet coefficients and filter out noise; and finally, reconstruct the signal. The wavelet threshold function mainly includes the soft threshold as shown in Equation (5) and the hard threshold as shown in Equation (6). η(w) = (w − sgn(w)T)I(|w| > T) (5) η(w) = wI(|w| > T) (6) As shown in Equation (6), the treatment method of the hard threshold is to keep the wavelet coefficients above the threshold unchanged and change the wavelet coefficients below the threshold to 0.…”

Section: Signal Reconstruction Based On Eemd and Wsstmentioning

confidence: 99%

“…The wavelet threshold function mainly includes the soft threshold as shown in Equation (5) and the hard threshold as shown in Equation (6). η(w) = (w − sgn(w)T)I(|w| > T) (5) η(w) = wI(|w| > T) (6) As shown in Equation (6), the treatment method of the hard threshold is to keep the wavelet coefficients above the threshold unchanged and change the wavelet coefficients below the threshold to 0. However, this "guillotine" method will cause changes in the wavelet domain and lead to sudden local changes in the noise reduction results.…”

Section: Signal Reconstruction Based On Eemd and Wsstmentioning

confidence: 99%

“…In addition to the frequently-used time domain [2] and frequency domain analysis [3], current signal denoising methods have also developed some very advanced time-frequency analysis methods, such as empirical mode decomposition (EMD) [4], blind source separation (BSS) [5], energy entropy [6], variational mode decomposition (VMD) [7], wavelet transformation (WT) [8], approximate entropy (ApEn) [9], etc. Nonlinear and nonstationary signals can be decomposed into multiple intrinsic mode of the sampled signal, and PCA cannot well retain the real information of the original signal.…”

Section: Introductionmentioning

confidence: 99%

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A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

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Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.Entropy 2020, 22, 290 2 of 28 functions (IMFs) by EMD [10]. Aiming at the shortcomings of the EMD, such as modal mixture and end effect, which affect the accuracy of signal decomposition [11], Wu et al. [12] improved the EMD method with auxiliary noise and proposed the ensemble empirical mode decomposition (EEMD) method.The EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis [13]. However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al. [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise. However, there is no uniform standard for the operation of the wavelet threshold method, which has a greater impact on the final result. The wavelet hard threshold denoising method generates discontinuous points and loses some vital information [17]. The wavelet soft threshold denoising method causes distortion phenomena, such as edge blur effect [18].Although the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but al...

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Section: Signal Reconstruction Based On Eemd and Wsstmentioning

confidence: 99%

Section: Signal Reconstruction Based On Eemd and Wsstmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

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“…Some methods developed from wavelet transform are restricted to the selection of the wavelet basis functions and decomposition layer. Singular spectrum analysis (SSA) is a kind of nonparametric spectral estimation method based on principal component analysis, and it can capture the high harmonic oscillation shapes based on data adaptive driven, which is suitable for dealing with the vibration signal with nonlinear and nonstationary signals [9,10]. Traditional SSA is generally divided into four steps: trajectory matrix construction, singular value decomposition (SVD), component grouping, and diagonal averaging.…”

Section: Introductionmentioning

confidence: 99%

Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

2019

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Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of planetary gear for the purpose of maintenance repair, predicting development trend, and avoiding sudden fault. This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN). SSD can automatically realize the embedding dimension selection and component grouping segmentation, and the original vibration signal being nonlinear and nonstationary can be decomposed into a series of singular spectrum decomposition components (SSDCs), adaptively. Then, the multiscale information dimension which combines multiscale analysis and fractal information dimension is proposed for quantifying and extracting the feature information contained in each SSDC. Finally, CNN is used to achieve the effective recognition of the degradation state of planetary gear. The experimental results show that the proposed method can accurately recognize the degradation state of planetary gear, and the overall recognition rate is up to 97.2%, of which the recognition rate of normal planetary gear reaches 100%.

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“…Popular terms such as Internet of Things and smart structures were coined as a result of the intersection between advances in other engineering disciplines with civil engineering to produce the new field of structural health monitoring (SHM). The field of SHM is now at a vital crossroads, where researchers are challenged to develop technologies for the monitoring and retrofit of older buildings and at the same time to push the boundaries of SHM through the creative use of cutting-edge technologies and data processing algorithms.This issue is a snapshot of the newest research in SHM for civil structures, and it includes a range of topics such as data processing algorithms to detect damage, modeling, and simulation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]; sensor development and experiments [16][17][18][19][20][21][22][23][24][25][26]; materials studies [27,28]; state-of-the-art reviews [29,30]; and case studies [31]. SHM is highly multi-disciplinary, and advances in other areas of study can likely be recruited for the progress of SHM.…”

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confidence: 99%

Structural Health Monitoring (SHM) of Civil Structures

2017

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As newer and more reliable ways of construction were developed, civilization began to spread out further and retain functional infrastructure for longer periods of time. Key building materials such as concrete ushered in a new era of civil engineering that enabled the rapid and low-cost construction of infrastructure that now serves as the backbone of modern society. Many of such buildings constructed in the 19th to the mid-20th century are still in operation today as a testament to the robustness of the civil structures enabled by the key building materials and methods. However, robustness has its limits, and the extended service life has inevitably led to the dangerous accumulation of damage in the infrastructure. Aging infrastructure is a problem met around the globe, and in the US, the National Academy of Engineering proclaimed the restoration and improvement of urban infrastructure to be one of the Grand Challenges of the 21st century.For the answer to the challenge of aging infrastructure, we look towards the development of advanced sensor and actuator technologies that hold the promise of heralding the next stage of evolution in civil infrastructure. Popular terms such as Internet of Things and smart structures were coined as a result of the intersection between advances in other engineering disciplines with civil engineering to produce the new field of structural health monitoring (SHM). The field of SHM is now at a vital crossroads, where researchers are challenged to develop technologies for the monitoring and retrofit of older buildings and at the same time to push the boundaries of SHM through the creative use of cutting-edge technologies and data processing algorithms.This issue is a snapshot of the newest research in SHM for civil structures, and it includes a range of topics such as data processing algorithms to detect damage, modeling, and simulation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]; sensor development and experiments [16][17][18][19][20][21][22][23][24][25][26]; materials studies [27,28]; state-of-the-art reviews [29,30]; and case studies [31]. SHM is highly multi-disciplinary, and advances in other areas of study can likely be recruited for the progress of SHM. The future of this field is very bright, and will be a driver for the coming futuristic, intelligent infrastructure.

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Improved Tensor-Based Singular Spectrum Analysis Based on Single Channel Blind Source Separation Algorithm and Its Application to Fault Diagnosis

Cited by 12 publications

References 35 publications

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN

Structural Health Monitoring (SHM) of Civil Structures

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