Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

Wang, Zhijian; Zheng, Likang; Wang, Junyuan; Du, Wenhua

doi:10.1155/2019/4031795

Cited by 38 publications

(27 citation statements)

References 70 publications

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“…Further optimizing the model and improving the estimation of image rotation angle are future directions of this work. In the future, this research can also be combined with many disciplines, such as artificial intelligence, criminal investigation, fault analysis [22,23], and so on.…”

Section: Discussionmentioning

confidence: 99%

Rotation Angle Estimation of JPEG Compressed Image by Cyclic Spectrum Analysis

et al. 2019

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Image rotation is a common auxiliary method of image tampering, which can make the forged image more realistic from the geometric perspective. Most algorithms of image rotation angle estimation employ the peak value on the Fourier spectrum; however, JPEG post-processing brings additional peak interferences to the spectrum, which has a great impact on algorithm performance. In this paper, angle estimation is carried out for images compressed by JPEG. Firstly, the Fourier cyclic spectrum of image covariance is calculated, followed by semi-soft threshold wavelet transform to eliminate the block artefacts brought by JPEG compression. According to the shortest distance principle in the range of the limited amplitude, the processed cyclic spectral data are sorted to select the peak points. Finally, according to the selected peak point, the corresponding position coordinates of the theoretical peak point are found, and the rotation angle of the image is estimated by the theoretical peak point. Experimental results demonstrate that the average absolute error of the proposed algorithm is significantly lower than that of the state-of-the-art methods investigated, which highlights the promising potential of the proposed method as an image resampling detector in practical forensics applications.algorithm. In general, the EM algorithm can only judge whether the image was resampled or not, but it cannot estimate the resampling factor quantitatively. Furthermore, depending on the periodicity of the interpolation signal and its derivative, Mahdian et al. [6] introduced Radon transform to analyze the change in covariance statistics and detect the rotation angle of an image. The Radon transform algorithm can only judge the rotation operation of the image qualitatively, but it cannot estimate the concrete rotation angle. In order to solve the problem of the above algorithm not being able to estimate the resampling factor, Gallagher et al. [7] estimated the scaling factors of images based on the fact that interpolation signals (linear and cubic interpolation) would introduce periodicity to the second derivative on variance. However, the defect of this algorithm lies in the inaccurate estimation of the image scaling factor and the inability to detect down-sampling operations. To avoid this problem, Wei et al. [8] improved Gallagher's algorithm, derived a detection formula for image down-sampling, and proposed a method to estimate rotation angle. However, the method was only limited to the detection of single resampling, and no rigorous theoretical proof was given for the formula of peak position. Vazquez-Padin et al.[9] applied a second-order cyclostationary method to detect the periodicity caused by resampling. Chen et al. [3,10] improved the theoretical derivation and experimental results of Vazquez-Padin et al. [9], and the further derivation of double resampling was presented. However, it resulted in larger errors when the rotation angle was small. Reference [11] exposed the resampling traces by increasing the gap between image a...

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Section: Discussionmentioning

confidence: 99%

Rotation Angle Estimation of JPEG Compressed Image by Cyclic Spectrum Analysis

et al. 2019

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“…(4) The position and brightness of the firefly with the greatest brightness are output, and the obtained X * i is used as the optimal scale of the filter. (5) Update the location of fireflies: Update the location of fireflies according to Equation (32). The flow chart of MED filter size optimization algorithm based on the firefly algorithm is shown in Figure 11, and the flow chart of the new method is shown in Figure 12.…”

Section: Entropy 2019 19 X For Peer Review 11 Of 23mentioning

confidence: 99%

“…EMD has the characteristics of orthogonality, completeness and self-adaptability, and has been widely used in signal processing and fault diagnosis. However, the existence of modal aliasing and endpoint effect limits its further promotion [25][26][27][28][29][30][31][32]. Ensemble empirical mode decomposition (EEMD) proposed by Wu and Huang in 2009 [33] can adaptively decompose complex mixed signals into a series of intrinsic mode functions (IMFs), which distribute different frequencies on different IMFs to achieve noise reduction.…”

Section: Introductionmentioning

confidence: 99%

Application of a Novel Adaptive Med Fault Diagnosis Method in Gearboxes

Guo

Han

et al. 2019

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Minimum entropy deconvolution (MED) is not effective in extracting fault features in strong noise environments, which can easily lead to misdiagnosis. Moreover, the noise reduction effect of MED is affected by the size of the filter. In the face of different vibration signals, the size of the filter is not adaptive. In order to improve the efficiency of MED fault feature extraction, this paper proposes a firefly optimization algorithm (FA) to improve the MED fault diagnosis method. Firstly, the original vibration signal is stratified by white noise-assisted singular spectral decomposition (SSD), and the stratified signal components are divided into residual signal components and noisy signal components by a detrended fluctuation analysis (DFA) algorithm. Then, the noisy components are preprocessed by an autoregressive (AR) model. Secondly, the envelope spectral entropy is proposed as the fitness function of the FA algorithm, and the filter size of MED is optimized by the FA algorithm. Finally, the preprocessed signal is denoised and the pulse enhanced with the proposed adaptive MED. The new method is validated by simulation experiments and practical engineering cases. The application results show that this method improves the shortcomings of MED and can extract fault features more effectively than the traditional MED method.

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“…Furthermore, many noises and other environmental factors make the fault information difficult to be detected. If this early failure continues to develop, it could reduce the operating stability of the machines, and even cause the catastrophic failures [2,3]. erefore, the accurate and reliable fault diagnosis of early local damage for the rolling bearings is of particular significance.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

et al. 2020

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Currently, study on the relevant methods of variational mode decomposition (VMD) is mainly focused on the selection of the number of decomposed modes and the bandwidth parameter using various optimization algorithms. Most of these methods utilize the genetic-like algorithms to quantitatively analyze these parameters, which increase the additional initial parameters and inevitably the computational burden due to ignoring the inherent characteristics of the VMD. From the perspective to locate the initial center frequency (ICF) during the VMD decomposition process, we propose an enhanced VMD with the guidance of envelope negentropy spectrum for bearing fault diagnosis, thus effectively avoiding the drawbacks of the current VMD-based algorithms. First, the ICF is coarsely located by envelope negentropy spectrum (ENS) and the fault-related modes are fast extracted by incorporating the ICF into the VMD. Then, the fault-related modes are adaptively optimized by adjusting the bandwidth parameters. Lastly, in order to identify fault-related features, the Hilbert envelope demodulation technique is used to analyze the optimal mode obtained by the proposed method. Analysis results of simulated and experimental data indicate that the proposed method is effective to extract the weak faulty characteristics of bearings and has advantage over some advanced methods. Moreover, a discussion on the extension of the proposed method is put forward to identify multicomponents for broadening its applied scope.

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Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

Cited by 38 publications

References 70 publications

Rotation Angle Estimation of JPEG Compressed Image by Cyclic Spectrum Analysis

Rotation Angle Estimation of JPEG Compressed Image by Cyclic Spectrum Analysis

Application of a Novel Adaptive Med Fault Diagnosis Method in Gearboxes

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

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