Wavelet Modulus Maxima Method Research Articles

Improved wavelet modulus maximum method for distributed optical fiber temperature sensing

The temperature measurement accuracy of the Raman distributed optical fiber temperature sensing system is an important metric. Aimed at the reduction of the temperature measurement error, which is caused by the noises in the sensor system, an improved wavelet modulus maximum method, which combine the 3sigm criterion with the traditional wavelet modulus maximum algorithm is proposed. In this proposed algorithm, the 3sigm value is used as the threshold for the highest decomposition layer of the wavelet modulus maximum method to judge whether it is a signal or noise, instead of using the ratio of the modulus maximum of the highest decomposition level to the number of decomposition levels to determine the threshold. We conduct distributed temperature measurement on a 10-km long multimode fiber, and analyze the experimental data by using the improved algorithm. The results show that the temperature measurement fluctuation obtained by improved method is 0.497°C on average. It is better than the 1.13°C that is obtained by the traditional wavelet modulus maximum algorithm. Compared with the traditional algorithms, the 3sigm criterion algorithm has improved the temperature measurement accuracy significantly. The improved algorithm provides a new idea for the selection of the threshold value, which makes the threshold selection more statistically.

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.

Research on an Lost Circulation Zone Location Method Based on Transient Pressure Wave.

Accurately identifying the location of loss zone after lost circulation is the key to subsequent plugging operation. In view of the difficulty of identifying the location of lost circulation zone, a method of identifying the location of loss zone by transient pressure wave signal is proposed. When lost circulation occurs, transient back pressure is applied to the wellhead at the surface choke manifold to produce transient pressure wave. The transient pressure wave propagates downward from the wellhead. The propagation process of transient pressure wave in an annulus system is analyzed, and the position of loss zone is determined according to the change of pressure signal at the choke manifold. Based on the simulation of this method, relevant experiments are also carried out. Aiming at the problem of excessive noise of the pressure wave signal collected in the experiment, variational modal decomposition (VMD) is used to decompose the signal into multiple band-limited intrinsic mode function (BIMF) components. Combined with a Hilbert spectrum, the time–frequency characteristics and energy distribution of each BIMF component are analyzed in turn. The main frequency component is selected to reconstruct the signal to achieve the denoising effect. On this basis, a wavelet modulus maxima method is used to decompose the denoised signal, extract the characteristic points of the signal, identify the loss circulation information in the signal, and then identify the thief zone position by a time–domain method. Through experimental verification, the existence of loss zone will affect the change trend of pressure wave; a VMD–wavelet modulus maxima algorithm can effectively remove the noise of the pressure wave signal and locate the pressure change point. The experimental recognition error range of this method is 0.10–9.22%, which has certain guiding significance for field application.

Changes in long-term properties and natural cycles of the Danube river level and flow induced by damming

In this paper we assessed changes in scaling properties of the river Danube level and flow data, associated with building of Djerdap/Iron Gates hydrological power plants positioned on the border of Romania and Serbia. We used detrended fluctuation analysis (DFA), wavelet transform spectral analysis (WTS) and wavelet modulus maxima method (WTMM) to investigate time series of measurements from hydrological stations in the vicinity of dams and in the area of up to 480 km upstream from dams, and time series of simulated NOAA-CIRES 20th Century Global Reanalysis precipitation data for the Djerdap/Iron Gates region. By comparing river dynamics during the periods before and after construction of dams, we were able to register changes in scaling that are different for recordings from upstream and from downstream (from dams) areas. We found that damming caused appearance of human-made or enhancement of natural cycles in the small time scales region, which largely influenced the change in temporal scaling in downstream recording stations. We additionally found disappearance or decline in the amplitude of large-time-scale cycles as a result of damming, which changed the dynamics of upstream data. The most prominent finding of our paper is a demonstration of a complete or partial loss of annual cycles in the upstream stations’ data that stems from the operation of the artificial water reservoir and extends as far as 220 km from dams. We discussed probable sources of such found changes in scaling, aiming to provide explanations that could be of use in future environmental assessments.

Research on adaptive modulus maxima selection of wavelet modulus maxima denoising

Microelectromechanical system (MEMS) accelerometers are small in size, low in power consumption and easily integrated. They can be used in intelligent hydraulic components to obtain the dynamic acceleration of the system and monitor the operating status of the system. In this study, based on the noise characteristics of MEMS accelerometer output signals, a wavelet modulus maxima denoising algorithm based on adaptive threshold estimation is proposed, in which SureShrink threshold estimation is used to choose the right modulus maxima. Then, the signal-to-noise ratio and mean-square-error are used as the evaluating indices of the denoising performance for the wavelet modulus maxima denoising based on SureShrink threshold estimation, the wavelet modulus maxima denoising based on BayesShrink threshold estimation and the normal wavelet modulus maxima denoising. The simulation results show that the wavelet modulus maxima denoising based on SureShrink threshold estimation has better denoising performance than the normal modulus maxima denoising and the wavelet modulus maxima method based on BayesShrink threshold estimation, and effectively eliminates the noise of MEMS accelerometer output signals.

Wavelet modulus maxima method for on-line wavelength location of pulsed lidar in CO_2 differential absorption lidar detection

Differential absorption lidar (DIAL) is an excellent technology for atmospheric CO2 detection. However, the accuracy and stability of a transmitted on-line wavelength are strictly required in a DIAL system. The fluctuation of a tunable pulsed laser system is relatively more serious than that of other laser sources, and this condition leads to a large measurement error for the lidar signal. These concerns pose a significant challenge in on-line wavelength calibration. This study proposes an alternative method based on wavelet modulus maxima for the accurate on-line wavelength calibration of a pulsed laser. Because of the different propagation characteristics of the wavelet transform modulus maxima between signal and noise, the singularities of a signal can be obtained by detection of the local modulus maxima in the wavelet transform maximum at fine scales. Simulated analysis shows that the method is more accurate than the general method such as quintic polynomial fitting and can steadily maintain high calibration precision at different signal-to-noise ratios (SNRs). Last, 16 groups of real experiments were conducted to verify the simulated analysis, which shows that the proposed method is an alternative for accurately calibrating an on-line wavelength. In addition, the proposed method is able to suppress noises in the process of wavelength calibration, which gives it an advantage in accurate on-line wavelength calibration with a low SNR.

Optical Correlation Recognition Technology of Small Moving Target Based on Wavelet Multi-Scale Edge Fusion

Moving target recognition has a wide application in varieties of domains and has received more attention in recent years. Among all the recognition methods, optical correlation technology, using hybrid optoelectronic joint transform correlator (HOJTC), has the merits of parallel, large capacity and high-speed, so it is considered as one of the most effective methods for moving target detection. But in the course of detecting, some targets are too small to be recognized by HOJTC. These small targets in cluttered background and strong noise may affect the brightness of the correlation peaks and have a serious influence on target recognition results. In order to solve this problem, this paper applies a wavelet multi-scale edge fusion algorithm which firstly extracts the edge at different scales by wavelet modulus maximum method, and then fuses the detected edge to obtain more edge information of the target. In addition, for the moving distortion problems, taking temporal state of the target as the template can achieve the template update. To prove this method, many detection experiments of small moving targets have been performed with optical correlation method. As an example a small target “boat” taking up only pixels is presented. The recognition results show that the brightness of the correlation peaks is enhanced and the target recognition ratio is increased. The conclusion can be drawn that applying this algorithm in optical correlation method can realize the small moving target recognition successfully and expand the application to scope of optical correlation technology.

Fault Diagnosis of the Wind Turbine Main Bearing through Multifractal Theory

Because the vibration signals of faulty wind turbine are non-linear and non-stationary, to obtain the obvious fault features become difficult. In this study, the incipient fault of the main bearing used in large scale wind turbine is studied by using a multifractal method based on the Wavelet Modulus Maxima (WTMM) method. The real vibration signals from the main bearings are analyzed using the multifractal spectrum. The spectrum of the vibration signals is quantified by spectral characteristics including its range and the Hölder exponent corresponding to the maximum dimension. The results show that the range of Hölder exponent of the main bearing which worked normally is much narrower. While the ranges of the vibration signals of the main bearing with incipient fault are wider. We also found that the fault features are different at various wind turbine rotational frequencies. Those demonstrate that the incipient fault features of main bearing of large scale wind turbine can be extract effectively using the multifractal spectrum obtained from WTMM method.

Detection of singularities in the pressure fluctuations of circulating fluidized beds based on wavelet modulus maximum method

Pressure fluctuations are often selected to analyze the dynamic changes in the gas–solid fluidized bed. In this study, the wavelet modulus maximum method is applied to analyze the pressure fluctuations in a circulating fluidized bed. Continuous wavelet transform is put on the pressure signals, then, maxima lines are extracted and the Lipschitz exponent, α, of every maximum line is calculated using the non-linear least-squares method. The mean of the negative α is adopted to describe the hydrodynamic characteristics in the circulating fluidized bed. The value of the mean of the negative α increases with the superficial gas velocity and turns stable when the flow regime is fast fluidization. This method has been tested effective under various operation conditions.