Noise Reduction for High-Accuracy Automatic Calibration of Resolver Signals via DWT-SVD Based Filter

Guo, Meishan; Wu, Zhong

doi:10.3390/electronics8050516

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…where m represents the number of matrix rows, n is the number of matrix columns, and N = m + n − 1. The Hankel matrix undergoes a singular value decomposition [27][28][29], represented as…”

Section: Asei-vmd Methodsmentioning

confidence: 99%

Electromagnetic Interference Cancellation in the Frequency Domain Based on the ASEI-VMD Method

Chen,

Jin,

Liu

et al. 2023

Electronics

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In electromagnetic compatibility (EMC) testing, accurately extracting the radiation emission characteristics from the Equipment Under Test (EUT) in complex electromagnetic environments remains a daunting task. This paper presents a solution by introducing of a frequency-domain electromagnetic interference cancellation method based on the Adaptive Singular Envelope Iterative Variational Mode Decomposition (ASEI-VMD). The process begins with the application of the Adjacent Singular Envelope Entropy Ratio (ASEER) as an evaluation metric to adaptively discern the number of decomposition layers and the penalty factor, allowing the effective decomposition of signals from various channels into modal signals sharing the same center frequency and bandwidth. The singular envelope entropy of each VMD mode is computed as the basis for assessing the efficacy of components in the VMD. This step distinguishes the signals encapsulating the EUT frequency information, which are then isolated for cancellation to retrieve the actual EUT-radiated emission signal. Simulation and experimental data validate the efficacy of this approach. Uniquely, it neither places demands on the testing site nor requires prior conditions for the EUT. The retrieved signal exhibits a correlation of over 96% with the source signal, maintaining a signal-power error below 3 dB. It is suitable for conducting electromagnetic radiation emission testing in complex environments.

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Section: Asei-vmd Methodsmentioning

confidence: 99%

Electromagnetic Interference Cancellation in the Frequency Domain Based on the ASEI-VMD Method

Chen,

Jin,

Liu

et al. 2023

Electronics

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“…Here, E m×n can be written in the form E m×n = T + Z, where T denotes the true useful signal needed in feature extraction, Z denotes the noisy signal, and T ∈ R m×n , Z ∈ R m×n . The process of denoising the signal X is the process that involves finding a matrix that best approximates the matrix T. From the theory of SVD and the matrix best approximation theorem in the sense of the Frobenius norm, it is known that the real useful signal is mainly reflected by the first q largest singular values [40], and the noise signal is mainly reflected by the remaining singular values. If the first q singular values are selected and they are processed by the inverse transform of the SVD, the matrix D n can be obtained.…”

Section: The Proposed Sveacsmentioning

confidence: 99%

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

Xu¹,

Shen²,

Jiang³

et al. 2022

Meas. Sci. Technol.

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It is usually difficult in extracting weak fault features from rolling bearing vibration signals under noise pollution. Aiming to this problem, a fault feature extraction approach for rolling bearing using improved singular spectrum decomposition (SSD) and singular value energy autocorrelation coefficient spectrum (SVEACS) is proposed. Firstly, to facilitate the determination of the optimal modal parameters in the singular spectrum decomposition algorithm, the number of singular spectrum decomposition layers is adaptively determined with an improved SSD algorithm based on permutation entropy. Then the optimal modal components are selected, and the proposed SVEACS is used to determine the order of singular value noise reduction. Finally, the envelope analysis is used to extract the accurate shock characteristics of the denoised signal. The results of the experiments on simulation and real signals indicate that, the proposed method can effectively extract the weak characteristics of the vibration signal under strong noise, and accurately diagnose the fault of rolling bearing.

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“…The literature addresses the modeling of PQ disturbances to verify the various detection methods in Section 2, and a series of efficient modeling algorithms have been proposed [11][12][13][14]. The unified setting of the disturbance start time is 0.07 s, and the end time is 0.14 s (pulse transient start time is 0.0996 and end time is 0.1004).…”

Section: Disturbances Modelsmentioning

confidence: 99%

Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

et al. 2020

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With the increasing proportion of various unbalanced loads in the power grid, power quality is seriously challenged. It is of great significance to effectively detect, analyze, and evaluate the power quality problems. First, this paper introduces the current situation of power quality (PQ) disturbance detection methods. It summarizes that the current PQ disturbance detection methods include Wavelet Transform (WT), Hilbert–Huang Transform (HHT), and Ensemble Empirical Mode Decomposition (EEMD). EEMD has a better detection accuracy, but its running time is longer. Therefore, to reduce the running time of the EEMD algorithm, this paper proposed two improvements: increasing the screening threshold and selecting piecewise cubic Hermite interpolation polynomial fitting. At the same time, the mathematical models of transient power quality disturbance and harmonic were established for comparative verification. The experimental results showed that the improved Ensemble Empirical Mode Decomposition (IEEMD) algorithm greatly reduced the running time of the algorithm on the premise of ensuring the detection accuracy. Hence, the improvement of this paper is of great significance for the industrial application of the EEMD algorithm.

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Noise Reduction for High-Accuracy Automatic Calibration of Resolver Signals via DWT-SVD Based Filter

Cited by 7 publications

References 27 publications

Electromagnetic Interference Cancellation in the Frequency Domain Based on the ASEI-VMD Method

Electromagnetic Interference Cancellation in the Frequency Domain Based on the ASEI-VMD Method

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

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