Chirp Rates Estimation for Multiple LFM Signals by DPT–SVD

Qi, Liangang; Shen, Zhenheng; Guo, Qiang; Wang, Yani; Mykola, Kaliuzhnyi

doi:10.1007/s00034-022-02225-x

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…The noise in the signal can be removed by setting the singular values that reflect the noise to zero. By adding and averaging the items corresponding to the reconstructed matrix, the denoised signal can be restored [13,14].…”

Section: Singular Value Decompositionmentioning

confidence: 99%

Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES

Wu,

Niu,

Wang

2023

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In this article, a method is proposed to effectively extract weak fault features and accurately diagnose faults in ball screws, even in the presence of strong background noise. This method combines singular value decomposition (SVD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the 1.5-dimensional spectrum (1.5D) to process and analyze fault vibration signals. The first step involves decomposing the fault signal using the SVD algorithm. The singular values are then screened, and the part of the screen containing more noise information is extracted to complete the first denoising step. The second step involves decomposing the signal after the initial denoising process using CEEMDAN and removing some of the false components from the intrinsic mode function (IMF) components, based on the kurtosis correlation function index. The signal is then reconstructed to complete the second denoising step. Finally, the denoised signal is analyzed using Teager energy operator demodulation and 1.5D spectral analysis to extract the fault frequency and determine the location of the fault in the ball screw. This method has been compared with other denoising methods, such as wavelet packet decomposition combined with CEEMDAN or SVD combined with variational mode decomposition (VMD), and the results show that under the condition of strong background noise, the proposed method can better extract the fault frequency of ball screw.

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Section: Singular Value Decompositionmentioning

confidence: 99%

Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES

Wu,

Niu,

Wang

2023

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Noise Immunity of Binary Chirp Signals

Dvornikov-jr.,

Selivanov,

Dvornikov

2024

jour

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The results of a study of the noise immunity of receiving signals with linear frequency modulation in telecommunication information transmission systems are considered. Analytical expressions for the synthesis of binary signals by linear frequency modulation with control parameters are obtained. The dependence of the structure of linear frequency modulation signals on the shaping parameters has been studied. The optimization problem of finding the maximum value of the Euclidean distance for linear frequency modulation signals of a binary structure has been solved by modeling. Differences have been established in the noise immunity of receiving linear frequency modulation signals in relation to the binary structures of opposite and orthogonal signals. Diagrams of time and spectral fragments of linear frequency modulation signals are presented, explaining the essence of the research results. Graphs are presented for comparative assessment of the noise immunity of receiving binary signals of various structures in terms of the bit error probability as a function of the signal-to-noise ratio in the channel. The essence of correlation processing of signals with a large base is revealed.

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Chirp Rates Estimation for Multiple LFM Signals by DPT–SVD

Cited by 2 publications

References 34 publications

Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES

Fault Feature Extraction Method of Ball Screw Based on Singular Value Decomposition, CEEMDAN and 1.5DTES

Noise Immunity of Binary Chirp Signals

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