Noise Reduction Method For Signals Research Articles

Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD

Defects in welds can affect the structural safety and reliability of workpieces. Currently, the method of using phased array ultrasonic inspection technology for non-destructive testing of weld structures with high detection efficiency, good sensitivity, and good visualization of the results is widely used. However, the defective A-scan data collected by the ultrasonic phased array detector inevitably contain noise data, including the test piece material structure noise, equipment noise, and environmental noise, which undoubtedly affects the analysis of the A-scan signal. In addition, when defects are interpreted, the presence of noise also interferes with the process, which affects the accuracy of the interpretation. Therefore, to enhance the accuracy of defect identification based on phased array ultrasonic inspection technology, we must prevent the series of consequences caused by misjudgments. In this study, ultrasonic phased array inspection experiments were carried out, and the specific process flow of ultrasonic phased array inspection of flat plate butt welds was summarized. Utilizing pre-fabricated flat plate butt specimen blocks containing five types of typical defects, defect A-sweep signals based on ultrasonic phased array inspection were obtained. Combining the sparrow optimization algorithm (SSA), variational mode decomposition (VMD), and wavelet packet decomposition (WPD), a defect signal noise reduction method based on parameter optimization was studied. A noise reduction study was carried out using the noise-added simulated signal, and the results indicated that the noise reduction method proposed in this paper had a better noise reduction effect and the proposed method could effectively retain the detailed features of the ultrasonic phased array defective A-scan signal and realize the noise reduction processing of the defective A-scan signal.

Research on U-Net seismic signal denoising combined with residual dense blocks

Aiming to address the limited noise reduction capabilities of conventional methods for reducing noise in seismic signals, the paper proposes a noise reduction model based on the RDBU-Net network. This model utilizes a residual dense block (RDB) instead of conventional convolutional layers in the U-Net network to enhance the feature extraction capacity for same-band noise, thereby elevating the signal-to-noise ratio (SNR) of seismic signals. The RDBU-Net model is trained, validated, and tested using the global seismic dataset from Stanford University. In comparison with the wavelet threshold method, the denoising RDB model, and the U-Net model, the RDBU-Net model demonstrates an improvement in SNRs by 7.82 dB, 6.13 dB, and 2.9 dB, respectively. Additionally, the root mean square errors are reduced by 0.4812, 0.3736, and 0.1938, and the correlation coefficients are enhanced by 0.3818, 0.2714, and 0.1205. The RDBU-Net model proposed in this study effectively enhances the SNR of seismic signals and offers fresh insights into eliminating noise within the same frequency band of seismic signals.

A novel joint denoising method for gear fault diagnosis with improved quaternion singular value decomposition

Singular value decomposition (SVD) has drawn increasing attention in recent years as an effective signal noise reduction method. However, it is inapplicable to multivariate signals with abundant fault information. Existing quaternion singular value decomposition (QSVD) can decompose multivariate signals simultaneously, but the methods based on QSVD dependent on the embedding dimension and reconstructed components order seriously, and they are not suitable to noisy signals. To solve the problem, a novel joint denoising method improved quaternion singular value decomposition (IQSVD) is proposed, which can determine two parameters adaptively. Firstly, to select the embedding dimension, the improved power spectral density (IPSD) is proposed with considering frequency features and time-domine traits together. And then the periodic intensity (PI) is used to determine the reconstructed components by searching the component composing the most abundant periodic part. Finally, combined with envelope spectrum analysis, the gear fault is diagnosed. The results of the gear simulated and experimental multivariate signals verify the effectiveness of proposed method.

Research on Denoising of GIS Ultra-High Frequency Partial Discharge Signal Based on VMD Combined with S-G Filter

Given the problem that it is easy to receive thermal noise interference from the electronic circuit and equipment circuit of the detection system in the current feature frequency detection of Gas Insulated Switchgear (GIS) equipment, this paper proposes an ultra-high frequency (UHF) signal noise reduction method based on variational mode decomposition (VMD) combined with Savitzky-Golay (S-G) filter and tests it through simulation. By using VMD combine with the S-G filter method, the signal-to-noise ratio (SNR) of signals can be effectively improved and the interference of white noise to signals can be reduced.

Research on the Signal Noise Reduction Method of Fish Electrophysiological Behavior Based on CEEMDAN with Improved Wavelet Thresholding

Research on the Signal Noise Reduction Method of Fish Electrophysiological Behavior Based on CEEMDAN with Improved Wavelet Thresholding

A novel method for noise reduction of blade tip timing signals based on sparse representation and dictionary learning

Blade Tip Timing Technology (BTT) is a new type of blade vibration measurement method with non-contact measurement capabilities, which is associated with high efficiency and convenience. However, due to under-sampling feature of the BTT signal, the method can only measure part of the vibration information. Therefore, various BTT spectral reconstruction algorithms have been developed based on the under-sampling feature and sparsity of the blade vibration spectrum. Because of the harsh working environment of the blades, BTT signal typically contains high-level Gaussian noise in actual measurement, which significantly reduces frequency-domain sparsity and even aliases the vibration modals, resulting in a decrease in the performance of sparse reconstruction algorithms. In this paper, a multi-sequences BTT signal noise reduction method is proposed, which is based on sparse representation to suppress Gaussian noise. Through appropriately modifying the constraint in the ℓ0 optimization problem, the method filters out the noise elements in the sparse vector of the BTT signal. Then, denoising is completed by sparse inverse transformation. Finally, a global average-based singular value decomposition dictionary learning algorithm (GA-K-SVD) is proposed to generate an over-complete sparse dictionary which is adaptable to the original signal, to increase the effectiveness of GA-K-SVD. At last, simulation and experience are carried out to verify the performance optimization effect for sparse reconstruction algorithm and the effectiveness of proposed noise reduction method.

Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter

Nanopore detection technology has now developed into an indispensable tool for single molecule experiments, in which useful information on molecular properties can be obtained from the ion current flow induced by individual molecules, but the relatively high background noise affects the data analysis. Therefore, a nanopore signal noise reduction method based on wavelet transform and Kalman filter is proposed, which can achieve multi-scale decomposition and optimal estimation of the nanopore signal. The real measurement shows that the Kalman filter based on the wavelet mode maxima method reduces the root mean square (RMS) of the background noise by 17.8%, and the noise reduction effect is better than the traditional Kalman filter method.

Seismic Signal Denoising Based on Adaptive Wavelet Modulus Maximum Method

Seismic noise suppression plays an important role in seismic data processing and interpretation. Aiming at remedying the problem of low quality of seismic data acquired by a seismometer, a novel denoising method based on wavelet maximum modulus and an adaptive threshold is designed. This adaptive wavelet maximum modulus (ATWMM) seismic signal noise reduction method is using the opposite polarity of the Lipschitz index with seismic signal and noise to extract the seismic signal of original data. Setting adaptive thresholding function related to wavelet decomposition scale to solve the problem of effective signal losing on a small decomposition scale. The experimental results show that the ATWMM method can extract more seismic signal from the noisy seismic data. Using RMSE and SNR evaluation, the synthetic Ricker seismic dataset experimental results show that the indexes are 0.0502 and 20.1617 dB. Compared with the wavelet modulus maximum (WMM) method, it has a 25.7% reduction and 14.6% increase. The real-field seismic data came from the JZW51 seismic monitoring station in China experimental results indicate that the proposed ATWMM method is effective in seismic signal denoising with SNR above 30.1855 dB of approximately 15.8% enhancement compared with the WMM method, that has improvement for quality of seismic data.

Adaptive quaternion multivariate local characteristic-scale decomposition and its application to gear fault diagnosis

Local characteristic-scale decomposition (LCD) has been widely applied in the field of gear fault diagnosis. However, LCD is only suitable for processing single channel signals and has poor noise robustness. Gear fault information collected by single channel of the sensor is not sufficient when the background noise is severe, based on which, a novel adaptive multivariate signal noise reduction method, quaternion singular value decomposition fault information spectrum (QSVDFIS), is defined in this paper. Based on QSVDFIS, a new multivariate signal decomposition method, adaptive quaternion multivariate local characteristic-scale decomposition (AQMLCD), is proposed in this paper. AQMLCD can adaptively reduce the noise of multivariate signal components, effectively fuse the fault information of each channel. Compared with multivariate empirical mode decomposition (MEMD), the noise robustness of AQMLCD is significantly improved, while the computation efficiency of AQMLCD is similar to that of MEMD. AQMLCD is applied to gear fault simulation and experimental signals, and the results illustrate the superiority of AQMLCD method.

Novel Signal Noise Reduction Method through Cluster Analysis, Applied to Photoplethysmography.

Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.

Noise Reduction of Steel Cord Conveyor Belt Defect Electromagnetic Signal by Combined Use of Improved Wavelet and EMD

In order to reduce the noise of a defect electromagnetic signal of the steel cord conveyor belt used in coal mines, a new signal noise reduction method by combined use of the improved threshold wavelet and Empirical Mode Decomposition (EMD) is proposed. Firstly, the denoising method based on the improved threshold wavelet is applied to reduce the noise of a defect electromagnetic signal obtained by an electromagnetic testing system. Then, the EMD is used to decompose the denoised signal and then the effective Intrinsic Mode Function (IMF) is extracted by the dominant eigenvalue strategy. Finally, the signal reconstruction is carried out by utilizing the obtained IMF. In order to verify the proposed noise reduction method, the experiments are carried out in two cases including the defective joint and steel wire rope break. The experimental results show that the proposed method in this paper obtains the higher Signal to Noise Ratio (SNR) for the defect electromagnetic signal noise reduction of steel cord conveyor belts.

A noise reduction method for signals from nonlinear systems

A numerical algorithm is presented for the purpose of reducing noise from a discretely sampled input signal where the underlying signal of interest has a broadband spectrum. It is designed to be useful even if the clean signal is contaminated with 100% or more noise (signal to noise ratio less than or equal to zero). The method is based on time delay embedding using coordinates generated by local low-pass filtering, which we call a low-pass embedding. The singular value decomposition is then used locally in embedding space to distinguish between the dynamics and the noise. The algorithm is evaluated for chaotic signals generated by the Lorenz and Rössler systems, to which Gaussian white noise has been added.