Wavelet Transform Denoising Research Articles

An Integrated Method for Tunnel Health Monitoring Data Analysis and Early Warning: Savitzky-Golay Smoothing and Wavelet Transform Denoising Processing.

A tunnel health monitoring (THM) system ensures safe operations and effective maintenance. However, how to effectively process and denoise several data collected by THM remains to be addressed, as well as safety early warning problems. Thus, an integrated method for Savitzky-Golay smoothing (SGS) and Wavelet Transform Denoising (WTD) was used to smooth data and filter noise, and the coefficient of the non-uniform variation method was proposed for early warning. The THM data, including four types of sensors, were attempted using the proposed method. Firstly, missing values, outliers, and detrend in the data were processed, and then the data were smoothed by SGS. Furthermore, data denoising was carried out by selecting wavelet basis functions, decomposition scales, and reconstruction. Finally, the coefficient of non-uniform variation was employed to calculate the yellow and red thresholds. In data smoothing, it was found that the Signal Noise Ratio (SNR) and Root Mean Square Error (RMSE) of SGS smoothing were superior to those of the moving average smoothing and five-point cubic smoothing by approximately 10% and 30%, respectively. An interesting phenomenon was discovered: the maximum and minimum values of the denoising effects with different wavelet basis functions after selection differed significantly, with the SNR differing by 14%, the RMSE by 8%, and the r by up to 80%. It was found that the wavelet basis functions vary, while the decomposition scales are consistently set at three layers. SGS and WTD can effectively reduce the complexity of the data while preserving its key characteristics, which has a good denoising effect. The yellow and red warning thresholds are categorized into conventional and critical controls, respectively. This early warning method dramatically improves the efficiency of tunnel safety control.

Adaptive noise removal for biological Raman spectra with low SNR

Raman data with low signal-to-noise ratio (SNR) are often obtained in the process of rapid or low-power acquisition for biological samples. The noise removal out of a biological Raman spectrum is laborious owing to numerous and superimposed Raman peaks, and it often takes a lot of time to find the optimal preproceed parameters. In this study, an adaptive denoising method for biological Raman data has been proposed, using three comparisons and a quantitative prediction model to show its simplicity and effectiveness. In this work, we decomposed Raman spectra of human skin and rice leaf on Hilbert vibration decomposition (HVD) method, to obtain a series of initial values, for example as peak position, intensity and full width at half maximum (FWHM), then fitted Raman spectrum according the Vogit function until the indicator function F reached the minimum value. The SNR and the root mean square error（RMSE) of HVD denoised spectrum were 57.5864 dB and 0.0108, and were both better than those of preproceed data (35.1826 dB, 0.0337; 32.4120 dB, 0.0352; 38.0685 dB, 0.0226) by Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Local Complementary Ensemble Empirical Mode Decomposition (LCEMD), compared with adaptive denoising methods, EMD, EEMD and LCEEMD, the improved algorithm had the advantages of no modal aliasing, no noise residue and independence of normalized permutation entropy. Then HVD denoised data of rice leaf samples were compressed by principal component analysis (PCA) and used for chlorophyll content prediction model. The correlation coefficient and standard error for prediction set of HVD +PCA model were 0.856 and 5.38, and were also better than those of non-adaptive preproceed data models (Mobing Average Smoothing, Gaussian Filter Smoothing, Median Filter Smoothing, Savitzky-Golay Smoothing and Wavelet Transform Denoising). The final experimental comparison showed that the HVD denoising method can also be applied to the Raman spectrum noise removal of inorganic samples, but better performances had been shown in the noise removal of complex biological Raman spectra. Accurate denoising of biological Raman spectrum is of great significance for accurate quantitative prediction and qualitative analysis, and no prior setting of the preproceed parameters makes the HVD denoising method easy to operate.

Research on Gear Signal Fault Diagnosis Based on Wavelet Transform Denoising

As an important part of modern machinery, gears are safe and reliable directly related to the normal operation of the mechanical system, and when the gears are abnormal, it is very important to carry out fault diagnosis in time and effectively. Wavelet transform is effective in signal time-frequency analysis and fault diagnosis. Based on the wavelet analysis method, this paper first filters and de-noises the vibration signal data of a certain type of gear, and then performs fault analysis. Time-frequency analysis of time-domain signals has a better effect on gear fault diagnosis.

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction.

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

Performance Analysis of Wavelet Transform De-noising Technique in Partial Discharge Signal based on Various Types of Mother Wavelet Via LabVIEW Software

Performance Analysis of Wavelet Transform De-noising Technique in Partial Discharge Signal based on Various Types of Mother Wavelet Via LabVIEW Software

Online estimation scheme of output capacitor's ESR and tan δ for Buck converter

Aluminium electrolytic capacitor (AEC) is one of the most age-affected components in power electronic converters, and its reliability has been a major concern. As the growth of service life, the equivalent series resistance (ESR) and dissipation factor (tanδ) of AEC increase due to the loss of electrolyte. Therefore, the online ESR estimation is critical for condition monitoring of AEC. Based on the inductor current ripple and capacitor voltage ripple, ESR calculation model and tanδ calculation model are derived, which are applied to both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). To implement the proposed scheme, a detailed solution based on wavelet transform denoising (WTD) is presented. The superiority of the proposed ESR identification scheme in consideration of sampling noise is verified. The proposed ESR estimation method can also be used in the case of non-linear loads such as converter load. The tests are carried out for capacitors with different ESRs. Simulation and experimental results verify the effectiveness of the method.

Lifting Wavelet Transform De-noising for Model Optimization of Vis-NIR Spectroscopy to Predict Wood Tracheid Length in Trees.

The data analysis of visible-near infrared (Vis-NIR) spectroscopy is critical for precise information extraction and prediction of fiber morphology. The objectives of this study were to discuss the de-noising of Vis-NIR spectra, taken from wood, to improve the prediction accuracy of tracheid length in Dahurian larch wood. Methods based on lifting wavelet transform (LWT) and local correlation maximization (LCM) algorithms were developed for optimal de-noising parameters and partial least squares (PLS) was employed as the prediction method. The results showed that: (1) The values of tracheid length in the study were generally high and had a great positive linear correlation with annual rings (R = 0.881), (2) the optimal de-noising parameters for larch wood based Vis-NIR spectra were Daubechies-2 (db2) mother wavelet with 4 decomposition levels while using a global fixed hard threshold based on LWT, and (3) the Vis-NIR model based on the optimal LWT de-noising parameters ( = 0.834, = 0.262, = 2.454) outperformed those based on the LWT coupled with LCM algorithm (LWT-LCM) ( = 0.816, = 0.276, = 2.331) and raw spectra ( = 0.822, = 0.271, = 2.370). Thus, the selection of appropriate LWT de-noising parameters could aid in extracting a useful signal for better prediction accuracy of tracheid length.

The Two-Dimensional Wavelet Transform De-noising and Combining with Side Scan Sonar Image

This paper puts forward an image de-noising method based on 2D wavelet transform with the application of the method in seabed identification data collection system. Two-dimensional haar wavelets in image processing presents a unified framework for wavelet image compression and combining with side scan sonar image. Seabed identification target have 7 target detection in side scan sonar imagery result. The vibration signals were analyzed to perform fault diagnosis. The obtained signal was time-domain signal. The experiment result shows that the application of 2D wavelet transform image de-noising algorithm can achieve good subjective and objective image quality and help to collect high quality data and analyze the images for the data center with optimum effects, the features from time-domain signal were extracted. 3 vectors were formed which are v1, v2, v3. In Haar wavelet retained energy is 93.8 %, so from the results, it has been concluded that Haar wavelet transform shows the best results in terms of Energy from De-noised Image processing with side scan sonar imagery.

Determination and Visualization of pH Values in Anaerobic Digestion of Water Hyacinth and Rice Straw Mixtures Using Hyperspectral Imaging with Wavelet Transform Denoising and Variable Selection.

Biomass energy represents a huge supplement for meeting current energy demands. A hyperspectral imaging system covering the spectral range of 874–1734 nm was used to determine the pH value of anaerobic digestion liquid produced by water hyacinth and rice straw mixtures used for methane production. Wavelet transform (WT) was used to reduce noises of the spectral data. Successive projections algorithm (SPA), random frog (RF) and variable importance in projection (VIP) were used to select 8, 15 and 20 optimal wavelengths for the pH value prediction, respectively. Partial least squares (PLS) and a back propagation neural network (BPNN) were used to build the calibration models on the full spectra and the optimal wavelengths. As a result, BPNN models performed better than the corresponding PLS models, and SPA-BPNN model gave the best performance with a correlation coefficient of prediction (rp) of 0.911 and root mean square error of prediction (RMSEP) of 0.0516. The results indicated the feasibility of using hyperspectral imaging to determine pH values during anaerobic digestion. Furthermore, a distribution map of the pH values was achieved by applying the SPA-BPNN model. The results in this study would help to develop an on-line monitoring system for biomass energy producing process by hyperspectral imaging.

Analysis of DWT signal denoising on various biomedical signals by neural network

A detailed analysis of Discrete Wavelet Transform (DWT) denoising and identification on various wavelet families and biomedical signals (ECG, EEG and EMG) is presented in this paper. The main intention of this work is to explore the wavelet function which is optimal for denoising the signals. Nevertheless, wavelet transforms offer better results for denoising biomedical signals, but identification is a crucial process. This paper proposes an artificial neural network in which the wavelet types are used to denoise the signals optimally by using a learning back propagation algorithm. Also the, performances of the various wavelet types are tabulated and compared with the existing techniques, in terms of the evaluation parameters signal to noise ratio, percent root mean square difference, mean-square error and compression ratio. The simulation results expose the efficiency of the proposed method for the denoising of biomedical signals.

Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS) technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS) time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1)the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2) the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3) The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.

Speech Enhancement Based on Bionic Wavelet Transform and Correlation Denoising

A new speech enhancement method based on bionic wavelet transform is presented here. Voice signals with noise would be bionic wavelet coefficients by bionic wavelet transform, then, the purpose of speech enhancement can be achieved by means of the bionic wavelet coefficients based on the improved correlation function processing. The simulation results show that the method under the condition of various noises is good speech enhancement effect. Keywords: Speech enhancement; BWT; Correlation de-noising

Enhance Accuracy in Pole Identification of System by Wavelet Transform De-Noising

Based on the multiscale character of wavelet transform, the method of wavelet transform de-noising (WTDN) is introduced to improve the signal-noise ratio (SNR) of sampled data and to enhance the accuracy of pole identification of system with N4SID (numerical algorithms for subspace state system identification) method. The WTDN method does not require extra limit of the frequency range for the processed signal, and does not need a prior estimate of impulse response for identified system. So it is especially suitable to de-noise the wide-band signal and the noisy impulse response of the blind system. The results of numerical simulation indicate that the WTDN method is reliable. The WTDN method is used to process the sampled data from actual preamplifier coupled to a gas detector. The experimental results show that the WTDN method improves effectively the SNR of sampled data, which can help to enhance the accuracy in pole identification of system with the N4SID method.