Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique

Liu, Zhenxing; Chang, Jianhua; Li, Hongxu; Zhang, Luyao; Chen, Sicheng

doi:10.1109/access.2020.3043182

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…The population size should be initialized at 20, and the maximum number of iterations should be set to 30. The search ranges for parameters K and α are set between [3,11] and [800,9000], respectively.…”

Section: A Variational Modal Decompositionmentioning

confidence: 99%

“…In January 2021, Liu Zhenxing et al put forward a denoising technique combining Variational Mode Decomposition with machine learning online optimization and interval threshold technology. This approach showed promising results in Lidar signal denoising [3]. September 2014 saw Salim Lahmiri proposing a mixed denoising method comprising Variational Mode Decomposition and Discrete Wavelet Transform, effectively eliminating additive Gaussian noise from electrocardiogram data [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Fault Detection Method for Rolling Bearings Based on Improved Variational Modal Decomposition Method

Huang,

Xu,

Cui

2024

IEEE Access

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To enhance the precision of rolling bearing fault detection and lessen the likelihood of safety mishaps, this paper proposes a fault detection method grounded in improved variational mode decomposition. This technique initially employs the Northern Eagle Algorithm to determine the optimal parameter value for variational mode decomposition, subsequently decomposing the signal. This is followed by the utilization of the Spearman correlation coefficient to differentiate between the effective component and the noise-dominant component. Finally, the wavelet packet decomposition is adopted to filter noise and yield the Hilbert envelope spectrum of the de-noised signal to ascertain the bearing's health status based on the extraction of characteristic fault frequencies and harmonics. The experimental findings illustrate that the enhanced variational mode decomposition technique not only escalates the inner ring signal's signal-tonoise ratio from -10.844dB to 8.4471dB and the outer ring signal's ratio from -4.5852dB to 3.0997dB but also reduces the error of outer ring fault detection from 3.14% to 0.37%, and improves the frequency of inner ring fault detection from a feature extraction inability to an error frequency of 0.45%.INDEX TERMS Fault detection, Improved variational modal decomposition, Northern eagle algorithm, Spearman correlation coefficient, Hilbert envelope spectrum, Characteristic frequency of faults.

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Section: A Variational Modal Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Fault Detection Method for Rolling Bearings Based on Improved Variational Modal Decomposition Method

Huang,

Xu,

Cui

2024

IEEE Access

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show abstract

“…where n(r) represents the measured signal return in photoelectron counts per second at range r. D[n(r)] is the dead time; n ap (r) is the after-pulsing; n b is background; O c (r) is the overlap factor; C represents the calibration constant of a dimensional system; E is the transmitted laser pulse energy; β is the backscatter coefficient; T is the atmospheric transmittance; NRB is the value-added data product (VAP) of ARM that is used for detecting clouds and aerosols, and the vertical resolution is 90 m. In this work, the interval thresholding technique is used to reduce noise interference [29]. NRB data below 4.37 km are used, and data from rain and fog meteorological conditions are discarded.…”

Section: Micro-pulse Lidar (Mpl)mentioning

confidence: 99%

“…Machine learning (ML)-which extracts features from the training data-is a powerful tool for classification and regression problems [23,24]. Moreover, ML has been widely used in target recognition [25], computer vision [26], and other fields [27][28][29], and has achieved remarkable results. In this paper, we regard the detection of ABLH as a cluster problem and explore how the appropriate algorithm can be used to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning

Chang

Liu

et al. 2022

Remote Sensing

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Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on cloud-free conditions or use other ancillary instruments due to strong interference from clouds or residual layer aerosols. In this paper, a machine learning method named the Mahalanobis transform K-near-means (MKnm) algorithm is first proposed to derive ABLH under complex atmospheric conditions using only LiDAR-based instruments. It was applied to the micro pulse LiDAR data obtained at the Southern Great Plains site of the Atmospheric Radiation Measurement (ARM) program. The diurnal cycles of ABLH from cloudy weather were detected by using the gradient method (GM), wavelet covariance transform method (WM), K-means, and MKnm. Meanwhile, the ABLH obtained by these four methods under cloud or residual layer conditions based on micropulse LiDAR data were compared with the reference height retrieved from radiosonde data. The results show that MKnm was good at tracking the diurnal variation of ABLH, and the ABLHs obtained by it have remarkable correlation coefficients and smaller mean absolute error and mean deviation with the radiosonde-derived ABLHs than those measured by other three methods. We conclude that MKnm is a promising algorithm to estimate ABLH under cloud or residual layer conditions.

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“…In 2019, Li et al integrated the whale optimization algorithm (WOA) and VMD to reduce noise of lidar echo signals, extending the detection range from 6 to 10 km [1]. In 2020, Liu et al combined VMD with machine learning online optimization (MLOO), producing better denoised results than EMD and other approaches [20]. However, the result is susceptible to the convergence behaviors, which are controlled by different optimization methods and fitness functions.…”

Section: Introductionmentioning

confidence: 99%

A novel Rayleigh lidar signal denoising algorithm for far-field noise suppression and high-accuracy retrieval

Wu,

Xu,

Zhong

et al. 2023

Real-Time Photonic Measurements, Data Management, and Processing VII

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Signal Denoising Method Combined With Variational Mode Decomposition, Machine Learning Online Optimization and the Interval Thresholding Technique

Cited by 8 publications

References 34 publications

Novel Fault Detection Method for Rolling Bearings Based on Improved Variational Modal Decomposition Method

Novel Fault Detection Method for Rolling Bearings Based on Improved Variational Modal Decomposition Method

Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning

A novel Rayleigh lidar signal denoising algorithm for far-field noise suppression and high-accuracy retrieval

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