Improved wavelet packet denoising algorithm using fuzzy threshold and correlation analysis for chaotic signals

Lu, Xihua; Bei, Guangxia

doi:10.1177/0142331220979229

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…To solve the above problems, many scholars have carried out related research. Liu et al [6] determined the optimal number of decomposition modes according to the difference of correlation function wavelet packet coefficients, and obtained the optimal wavelet basis with logarithmic energy entropy as the cost function to avoid the performance limitation of wavelet packet decomposition due to manual parameter setting. Lu et al [7] proposed a Mirror Extension Variational Modal Decomposition (MVMD) to suppress end effects.…”

Section: ) Signal Decomposition Methodsmentioning

confidence: 99%

Intelligent Diagnosis of Rolling Bearing Based on ICEEMDAN-WTD of Noise Reduction and Multi-Strategy Fusion Optimization SCNs

Li,

Wu,

Liu

et al. 2024

IEEE Access

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Aiming at the problem of noise interference leading to poor fault diagnosis effect of rolling bearing, a two-stage signal noise reduction method based on multi-strategy coati optimization algorithm (MFCOA) optimized ICEEMDAN combined with wavelet threshold denoising (ICEEMDAN-WTD) is proposed. The MFCOA-optimized stochastic configured networks (MFCOA-SCNs) is used for fault type identification. Firstly, ICEEMDAN is used to decompose noisy signals into several IMF signals, and then a wavelet threshold denoising method is used to process IMF signals whose arrangement entropy is lower than the pre-set value to obtain reconstructed signals. In this process, the ICEEMDAN decomposition is affected by the number of white noise additions and the degree of addition. Therefore, MFCOA is introduced to optimize the parameters to ensure the effect of signal noise reduction. Secondly, ICEEMDAN is applied to the reconstructed signal, and the entropy of IMF signal samples of each order is calculated to obtain the fault characteristics in different simultaneous frequency domains. Finally, the scale factor λ and regularization coefficient r in SCNs are optimized by MFCOA, and the sample entropy feature is used as the input of the MFCOA-SCNs model to achieve fault identification. Several simulation experiments have verified that the proposed method can effectively reduce the impact of noise in bearing signals, and the experimental results are better than other methods, and the diagnostic accuracy is better.

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Section: ) Signal Decomposition Methodsmentioning

confidence: 99%

Intelligent Diagnosis of Rolling Bearing Based on ICEEMDAN-WTD of Noise Reduction and Multi-Strategy Fusion Optimization SCNs

Li,

Wu,

Liu

et al. 2024

IEEE Access

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“…Gu et al improved the wavelet threshold based on variational mode decomposition and calculated the optimal denoising threshold by minimizing Stein unbiased risk estimation, achieving good denoising results 13 . Liu et al used wavelet packets to decompose chaotic signals and then used fuzzy analysis to construct wavelet packets the denoising thresholds to achieve the denoising of chaotic signals 14 .…”

Section: Introductionmentioning

confidence: 99%

Chaotic signal denoising based on energy selection TQWT and adaptive SVD

Yang,

Wang

2023

Sci Rep

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Aiming at the problem of denoising chaotic signals with low signal-to-noise ratio and unknown dynamic system parameters, a new chaotic signal denoising algorithm is proposed, which combines adjustable Q-factor wavelet transform (TQWT) and adaptive singular value decomposition (ASVD). This method uses TQWT to decompose the noisy chaotic signal. According to the maximum wavelet entropy theory and energy threshold rule, the subband of TQWT is accurately divided into signal subband and noise subband. For noise subbands, adaptive SVD is used to denoise them, to achieve preliminary denoising. In ASVD, the standard deviation of the singular value subset is used to determine the effective reconstruction order to improve the noise suppression effect. To further remove noise in the signal subband, TQWT reconstruction is performed on the preliminarily denoised signal, and ASVD is used to denoise the reconstructed signal again to obtain the chaotic signal after secondary denoising. Chua's simulated signal and four kinds of underwater radiated noise measured by TQWT-ASVD were denoised, and compared with the SVD denoising method, TQWT denoising method, complete ensemble empirical mode decomposition with adaptive noise and threshold denoising method (CEEMDAN-WT) and modified ensemble empirical mode decomposition combined with least squares denoising method (MEEMD-LMS), The experimental results show that the TQWT-ASVD method can reduce the noise of chaotic signals more effectively. Compared with SVD, TQWT, CEEMDAN-WT, MEEMD-LMS, and Chua's signal denoising method, the signal-to-noise ratio (SNR) of this method increased by 23.22%, 26.46%, 18.79%, 16.11% the root mean square error (RMSE) decreased by 32.53%,39.48%, 30.96%, 27.94%, and the row entropy (PE) decreased by 40.44%, 41.96%, 22.78%, 20.59%; After reducing the radiation noise of cargo ships, the PE value of this method is reduced by 13.91%, 10.18%, 10.88%, 8.68% respectively, and the FE value is reduced by 33.66%, 31.42%, 26.98%, 21.32% respectively.

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“…In the actual process of measuring the vibration signal, noise is an unavoidable interference, so the measured signal will inevitably interfere with different degrees of noise, which brings great difficulty to the feature extraction [16]. In order to reduce the interference of noise, many methods have been proposed by researchers.…”

Section: Introductionmentioning

confidence: 99%

Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1

Dai

Wang

et al. 2022

Shock and Vibration

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In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis methods are used, which cannot meet the high-precision diagnosis requirements. Therefore, a method is developed to identify the defect types and defect degrees of the gear-shaft-bearing system efficiently. In this method, the vibration signals are collected using multiple sensors, the dual-tree complex wavelet and the optimal weighting factor (OWF) methods are used for the data layer fusion, and the preprocessing is realized through wavelet transform and FFT. A learning model based on two-stream CNN composed of 1D-CNN and 2D-CNN is established, and the obtained wavelet time-frequency map and FFT spectrum are used as the input. Then, the trained features from the output of the connected layer are classified by the SVM. Compared with the OWF-1DCNN and OWF-2DCNN models, the time consumption of the OWF-TSCNN model is increased by 14.5%–26.6%, and the convergence speed of the network is decreased. However, its accuracy reaches 100% and 99.83% in the training set and test set, and the loss entropy and over-fitting rate are also greatly reduced. The feature extraction ability and generalization ability of the OWF-TSCNN model are increased, reaching 100% diagnosis accuracy on different defect types and defect degrees, which is more suitable for defect diagnosis of the gear-shaft-bearing system.

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Improved wavelet packet denoising algorithm using fuzzy threshold and correlation analysis for chaotic signals

Cited by 15 publications

References 19 publications

Intelligent Diagnosis of Rolling Bearing Based on ICEEMDAN-WTD of Noise Reduction and Multi-Strategy Fusion Optimization SCNs

Intelligent Diagnosis of Rolling Bearing Based on ICEEMDAN-WTD of Noise Reduction and Multi-Strategy Fusion Optimization SCNs

Chaotic signal denoising based on energy selection TQWT and adaptive SVD

Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1

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