Wavelet Denoising Analysis Research Articles

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

Research on Data Correlation in Structural Health Monitoring System

In structural health monitoring system, data analysis is one of the most important parts. It is mainly for processing and analysis of the collected data, among of which, the correlation analysis of the collected data can be used to verify the feasibility of the system. This paper applies the method of wavelet de-noising analysis to reduce signal noise and utilizes MATLAB and LabVIEW to calculate the cross-correlation coefficient in simulation statistics independently. To verify the feasibility of correlation analysis method and the data processing, simulation study is finished based on sampled data, which are the information of measuring points of strain and temperature from the Baishazhou Bridge. The cross-correlation coefficients between various signals can provide the reference to the whole health status of the civil engineering structure, and then enhance the accuracy of structural health assessment.

Condition Monitoring of Rope-less Elevator Braking System Based on Wavelet Denoising

The rope-less elevator driven by Permanent Magnet Linear Synchronous Motor (PMLSM) is a new technology for high-rise buildings. In the practical operation, PMLSM inevitably encounters various kinds of turbulences which directly affect the rope-less elevator’s operation. The braking system is important for rope-less elevator safe and steady operation. Usually, the ordinary inspections are not able to detect some early stage faults in the elevator braking system. Based on the analyzing of braking system common faults, the article proposes a rope-less elevator braking system monitoring scheme and presents a method that uses air gap sensor, hydraulic pressure transducer and disc spring pressure sensor in order to detect running condition of braking system. The sampling signals are inevitably influenced by noise, which degrades the accuracy and precision of an analysis. De-noising by soft-thresholding is a new signal inspection method in wavelet analysis. On the base of briefly introducing wavelet de-noising theory, a wavelet de-noising analysis method is presented. The study shows that the useful information reflecting the condition of braking system can be effectively extracted from highly noised signals by soft-thresholding de-noising method.

A simultaneous successive linear estimator and a guide for hydraulic tomography analysis

In this study, a geostatistically based estimator is developed that simultaneously includes all observed transient hydrographs from hydraulic tomography to map aquifer heterogeneity. To analyze tomography data, a data preprocessing procedure (including diagnosing and wavelet denoising analysis) is recommended. A least squares approach is then introduced to estimate effective parameters and spatial statistics of heterogeneity that are the required inputs for the geostatistical estimator. Since wavelet denoising does not completely remove noise from observed hydrographs, a stopping criterion is established to avoid overexploitation of the imperfect hydrographs. The estimator and the procedures are then tested in a synthetic, cross‐sectional aquifer with hierarchical heterogeneity and a vertical sandbox with prearranged heterogeneity. Results of the test indicate that with this estimator and preprocessing procedures, hydraulic tomography can effectively map hierarchical heterogeneity in the synthetic aquifer as well as in the sandbox. In addition, the study shows that using the estimated hydraulic conductivity and specific storage fields of the sandbox, the classic groundwater flow model accurately predicts temporal and spatial distributions of drawdown induced by an independent pumping event in the sandbox. On the other hand, the classic groundwater flow model yields less satisfactory results when equivalent homogeneous properties of the sandbox are used.

Wavelet-based partial discharge image denoising

An application of wavelet-based denoising to phase-resolved partial discharge images is presented. The basic principles of wavelet denoising analysis, with a special focus on image decomposition, as well as examples of hard- and soft denoising thresholding are reported. For the purposes of decomposition, the Deabuchies wavelet and wavelet packets at different levels were applied. Simulations are discussed and the results obtained during online measurements on a 6 kV/200 kW motor are presented. The method described is especially suited to cases in which an external additive noise uncorrelated with a partial discharge (PD) signal is present during acquisition, for example, in cables, transformers, rotating machines and gas-insulated switchgears. The fundamental issue in image recovery using wavelet denoising seems to be the choice of the threshold value and the type of the wavelet. Proper preprocessing is crucial prior to pattern recognition on the basis of a correlation with predefined PD forms. In addition, wavelet decomposition could be treated as lossy image compression in applications such as image internet transfer to/from external databases, in which only wavelet coefficients could be sent discarding the ones below a certain threshold level. The method presented can be applied during PD acquisition, for example, in high voltage cables, transformers, rotating machines and gas-insulated switchgears. The wavelet denoising processing will definitely find future applications in PD analysers, besides the boxcar accumulation method and spatial or FFT-based digital filtering

Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study

Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced.

A noise reduction technique for on-line detection and location ofpartial discharges in high voltage cable networks

Partial discharge (PD) measurements taken on-site are frequentlyaffected by noise due to external disturbances. This paper describes a noisereduction method that may be used to isolate the PDs from the noise. The basicprinciples of wavelet de-noising analysis are presented. The PDs are thenprocessed to allow accurate location of the PD. This method of location willalso be presented.