Wavelet Denoising Applied to Hardware Redundant Systems for Rolling Element Bearing Fault Detection

This work presents a novel wavelet-based denoising technique for improving the signal-to-noise ratio(SNR)of nonsteady vibration signals in hardware redundant systems.The proposed method utilizes the relationship between redundant hardware components to effectively separate fault-related components from the vibration signature,thus enhancing fault detection accuracy.The study evaluates the proposed technique on two mechanically identical subsystems that are simultaneously controlled under the same speed and load inputs,with and without the proposed denoising step.The results demonstrate an increase in detection accuracy when incorporating the proposed denoising method into a fault detection system designed for hardware redundant machinery.This work is original in its application of a new method for improving performance when using residual analysis for fault detection in hardware redundant machinery configurations.Moreover,the proposed methodology is applicable to nonstationary equipment that experiences changes in both speed and load.

Lift fin stabilizers based on data fusion with wavelet denoising technology

Fin stabilizers with fin-lift feedback control can shield the mapping error of calculation between the fin angle and fin lift force,which is in the fin stabilizer with fin-angle feedback control.In practice,there are some technical difficulties in lift fin stabilizers,such as lift force detection and lift force sensor installation,so it cannot achieve the good antirolling performance.Therefore,a fin stabilizer system with fin-lift/fin-angle integrated control is brought forward.Data fusion based on wavelet denoising technology is employed in the system,which combines lift with fin angle local information from two sensors with different frequency ranges in order to eliminate redundant and contradictory information,and using complementary information to obtain the relative integrity of the lift force signal.The system model is established in this paper,and the fusion signal and the antirolling performance of this model are simulated respectively.The result shows that the control system can meet the antirolling need in different sea situations.

NEW TECHNOLOGY FOR FAULT DIAGNOSIS BASED ON WAVELET DENOISING AND MODIFIED EXPONENTIAL TIME-FREQUENCY DISTRIBUTION

Fast wavelet multi-resolution analysis (wavelet MRA) provides a effective tool for analyzing and canceling disturbing components in original signal. Because of its exponential frequency axis, this method isn't suitable for extracting harmonic components. The modified exponential time-frequency distribution ( MED) overcomes the problems of Wigner distribution( WD) ,can suppress cross-terms and cancel noise further more. MED provides high resolution in both time and frequency domains, so it can make out weak period impulse components fmm signal with mighty harmonic components. According to the 'time' behavior, together with 'frequency' behavior in one figure,the essential structure of a signal is revealed clearly. According to the analysis of algorithm and fault diagnosis example, the joint of wavelet MRA and MED is a powerful tool for fault diagnosis.

Estimation of chlorophyll content in maize canopy using wavelet denoising and SVR method

In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy technology.The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise influence.Firstly,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition levels.As a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal.The MSC method was used to eliminate the scattering effect after denoising.Then three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 nm.Finally,the chlorophyll content estimation model was developed by using support vector regression(SVR)method.The calibration Rc2 of the SVR model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/L.The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.

Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.

Image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering

In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising. First, the wavelet transform is adopted to decompose the image of refuge chamber, of which low frequency component remains unchanged. Then, three high-frequency components are treated by bilateral filtering, and the image is reconstructed. The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image, while providing better visual effect. This is superior to using either bilateral filtering or wavelet transform alone. It is useful for perfecting emergency refuge system of coal mines.

New Wavelet Threshold Denoising Method in Noisy Blind Source Separation

In general conditions, most blind source separation algorithms are established on noisy-free model and ignore the noise that affects the quality of separated sources. Firstly, this paper introduces an improved natural gradient algorithm based on bias removal technology to estimate the demixing matrix under noisy environment. Then the discrete wavelet transform technology is applied to the separated signals to further remove noise. In order to improve the separation effect, this paper analyzes the deficiency of hard threshold and soft threshold, and proposes a new wavelet threshold function based on the wavelet decomposition and reconfiguration. The simulations have verified that this method improves the signal noise ratio （SNR） of the separation results and the separation precision.

Application of Wavelet Transform in the Prediction of Navel Orange Vitamin C Content by Near-Infrared Spectroscopy

This study was to search for an approach for rapid measurement of orange vitamin C （Vc） content. By using different decomposing levels of Daubechies 3 wavelet transform, the near-infrared spectra signals obtained from intact fruits of 100 navel orange samples were denoised, and the results of the predicted Vc contents for the corresponding samples determined by the reconstructed spectra after denoising were validated by means of PLS-CV （partial least squared-cross validation）. It was shown that the prediction effects verified by PLS-CV analysis varied when different wavelet transform decomposing levels were employed. At the wavelet decomposing level 4, the best prediction effect was obtained, with the correlation coefficient R between the prediction and true values being 0.9574 and the expected variance RMSECV being as low as 3.9 mg 100 g^-1. Furthermore, the 11 different approaches for the pretreatment of the near-infrared spectrum were compared. It was found that the calibration model established by PLS using spectra pretreated by wavelet transform denoising provided the best prediction for Vc content, exhibiting the highest correlation between the prediction and true values by cross validation. In conclusion, the near infrared spectral model denoised by means of wavelet transform can be used for accurate, rapid, and nondestructive quantitative analysis on navel orange Vc content.

R-L Method and BLS-GSM Denoising for Penumbra Image Reconstruction

When neutron yield is very low, reconstruction of coding penumbra image is rather difficult. In this paper, low-yield （109） 14 MeV neutron penumbra imaging was simulated by Monte Carlo method. The Richardson Lucy （R-L） iteration method was proposed to incorporated with Bayesian least square-Gaussian scale mixture model （BLS-GSM） wavelet denoising for the simulated image. Optimal number of R-L iterations was gotten by a large number of tests. The results show that compared with Wiener method and median filter denoising, this method is better in restraining background noise, the correlation coefficient Rsr between the reconstructed and the real images is larger, and the reconstruction result is better.

基于EEMD、相关系数、排列熵和小波阈值去噪的新型水下声学信号去噪方法

The complexities of the marine environment and the unique characteristics of underwater channels pose challenges in obtaining reliable signals underwater,necessitating the filtration of underwater acoustic noise.Herein,an underwater acoustic signal denoising method based on ensemble empirical mode decomposition(EEMD),correlation coefficient(CC),permutation entropy(PE),and wavelet threshold denoising(WTD)is proposed.Furthermore,simulation experiments are conducted using simulated and real underwater acoustic data.The experimental results reveal that the proposed denoising method outperforms other previous methods in terms of signal-to-noise ratio,root mean square error,and CC.The proposed method eliminates noise and retains valuable information in the signal.

Filtering of long-term dependent fractal noise in fiber optic gyroscope

Stochastic noises of fiber optic gyroscope （FOG） mainly contain white noise and fractal noise whose long-term dependent component causes FOG a rather slow drift. In order to eliminate this component, a two-step filtering methodology is proposed. Firstly, fractional differencing （FD） method is introduced to trans-form fractal noise into fractional white noise based on the estima-tion of Hurst exponent for long-term dependent fractal process, which together with the existing white noise make up of a gener-alized white noise. Further, an improved denoising algorithm of wavelet maxima is developed to suppress the generalized white noise. Experimental results show that the basic noise terms of FOG greatly decrease, and especially the slow drift is restrained effectively. The proposed methodology provides a promising ap-proach for filtering long-term dependent fractal noise.

Short-term tunnel-settlement prediction based on Bayesian wavelet:a probability analysis method

As urbanization accelerates,the metro has become an important means of transportation.Considering the safety problems caused by metro construction,ground settlement needs to be monitored and predicted regularly,especially when a new metro line crosses an existing one.In this paper,we propose a settlement-probability prediction model with a Bayesian emulator(BE)based on the Gaussian prior(GP),that is,a GPBE.In addition,considering the distortion characteristics of monitoring data,the data is denoised using wavelet decomposition(WD),so the final prediction model is WD-GPBE.In particular,the effects of different prediction ratios and moving windows on prediction performance are explored,and the optimal number of moving windows is determined.In addition,the predicted value for GPBE based on the original data is compared with the predicted value for WD-GPBE based on the denoised data.One year of settlement-monitoring data collected by a structural health monitoring(SHM)system installed on the Nanjing Metro is used to demonstrate the effectiveness of WDGPBE and GPBE for predicting settlement.

Quantitative analysis of ammonium salts in coking industrial liquid waste treatment process based on Raman spectroscopy

Quantitative analysis of ammonium salts in the process of coking industrial liquid waste treatment is successfully performed based on a compact Raman spectrometer combined with partial least square(PLS) method. Two main components(NH4SCN and(NH4)2S2O3) of the industrial mixture are investigated. During the data preprocessing, wavelet denoising and an internal standard normalization method are employed to improve the predicting ability of PLS models. Moreover,the PLS models with different characteristic bands for each component are studied to choose a best resolution. The internal and external calibration results of the validated model show a mass percentage error below 1% for both components.Finally, the repeatabilities and reproducibilities of Raman and reference titration measurements are also discussed.

Smooth pulse recovery based on hybrid wavelet threshold denoising and first derivative adaptive smoothing filter

Based on the pulse-shaping unit in the front end of high-power laser facilities,we propose a new hybrid scheme in a closed-loop control system including wavelet threshold denoising for pretreatment and a first derivative adaptive smoothing filter for smooth pulse recovery,so as to effectively restrain the influence of electrical noise and FM-to-AM modulation in the time–power curve,and enhance the calibration accuracy of the pulse shape in the feedback control system.The related simulation and experiment results show that the proposed scheme can obtain a better shaping effect on the high-contrast temporal shape in comparison with the cumulative average algorithm and orthogonal matching pursuit algorithm combined with a traditional smoothing filter.The implementation of the hybrid scheme mechanism increased the signal-to-noise ratio of the laser pulse from about 11 dB to 30 dB,and the filtered pulse is smooth without modulation,with smoothness of about 98.8%.

Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm

Aiming at the current problems of high failure rate and low diagnostic efficiency of railway point machines(RPMs)in the railway industry,a short-time method of fault diagnosis is proposed.Considering the effect of noise on power signals in the data acquisition process of the railway centralized signaling monitoring(CSM)system,this study utilizes wavelet threshold denoising to eliminate interference.The results show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power signals.Then in order to attain a lighter weight and shorten the running time of the diagnosis model,Mallat wavelet decomposition and artificial immune algorithm are applied to RPM fault diagnosis.Finally,voluminous experiments using veritable power signals collected from CSM are introduced,which show that combining these methods can procure higher precision of RPMs and curtail fault diagnosis time.This substantiates the validity and feasibility of the presented approach.

A New Denoising Technique for Capillary Electrophoresis Signals

Capillary electrophoresis (CE) is a powerful analytical tool in chemistry. Thus, it is valuable to solve the denoising of CE signals. A new denoising method called MWDA which employs Mexican Hat wavelet is presented. It is an efficient chemometrics technique and has been applied successfully in processing CE signals. Useful information can be extracted even from signals of S/N =1. After denoising, the peak positions are unchanged and the relative errors of peak height are less than 3%.

A FIELD TEST DATA RESEARCH BASED ON A NEW HYDRAULIC PARAMETERS QUICK TEST TECHNOLOGY

The hydraulic parameter quick test system in accordance with the oscillation principle can overcome the shortcomings of traditional methods in determining hydraulic parameters which,besides having complex hydrogeological restrictions and needing complex equipment in the field,are often both time and energy consuming,and high cost.Based on the hydrogeological conditions in the north anchorage of Taizhou Yangtze River Highway Bridge,a quick hierarchical test is carried out for different depths of single-hole drilling to verify the function of the system in this case.Meanwhile,with a purification of the raw data gathered in the test by wavelet denoising,the hydraulic parameters are worked out by adopting the Kipp model.A comparison between the Kipp model results and stratigraphic data as well as pumping test results justifies the reliability and accuracy of the new method.The study finds other advantages of the method like high efficiency in operation,flexibility in stratified test,as well as environmental-friendliness.Therefore,the technology is of significant practical value and application prospect.

Robustness of ventilation systems in the control of walking-induced indoor fluctuations: Method development and case study

Walking-induced fluctuations have a significant influence on indoor airflow and pollutant dispersion.This study developed a method to quantify the robustness of ventilation systems in the control of walking-induced fluctuation control.Experiments were conducted in a full-scale chamber with four different kinds of ventilation systems:ceiling supply and side return(CS),ceiling supply and ceiling return(CC),side supply and ceiling return(SC),and side supply and side return(SS).The measured temperature,flow and pollutant field data was(1)denoised by FFT filtering or wavelet transform;(2)fitted by a Gaussian function;(3)feature-extracted for the range and time scale disturbance;and then(4)used to calculate the range scale and time scale robustness for different ventilation systems with dimensionless equations developed in this study.The selection processes for FFT filtering and wavelet transform,FFT filter cut-off frequency,wavelet function,and decomposition layers are also discussed,as well as the threshold for wavelet denoising,which can be adjusted accordingly if the walking frequency or sampling frequency differs from that in other studies.The results show that for the flow and pollutant fields,the use of a ventilation system can increase the range scale robustness by 19.7%-39.4% and 10.0%-38.8%,respectively;and the SS system was 7.0%-25.7% more robust than the other three ventilation systems.However,all four kinds of ventilation systems had a very limited effect in controlling the time scale disturbance.

Extraction of Submerged Topographic Line in B-Scan Ultrasonic Image

The analysis of underwater topography ultrasonic image, which can be obtained by using B-scan ultrasound imaging technique, is the basis and important work in topography study. In this paper, we present a novel self-adapted method for the extraction of submerged topographic line. Since the distribution of gray values in the image has a mutation process, this feature is used to appropriately track the topographic line of imaging band, and wavelet denoising method is applied to denoise the obtained lines. The described method also takes the continuity of topography into consideration during tracking procedure. The results show that the extraction error is within 2-pixel width（approximate 1 mm）. This method is suitable for the extraction of current model topographic lines with the advantages of good self-adaption, high speed and high resolution.