A Novel Denoising Algorithm of Electromagnetic Ultrasonic Detection Signal Based on Improved EEMD Method

In this paper, we propose a new denoising algorithm for electromagnetic ultrasonic signals based on the improved EEMD method, which can adaptively adjust for added noise and average times in different noisy environments, so that the effect of the residual difference of white noise on the results can be eliminated as far as possible. First, the way to add white noise in the EEMD method is processed, and then the permutation entropy algorithm is used to identify the nature of the components obtained during the decomposition. Then the wavelet transform modulus maximum denoising method is used to deal with the IMF components of the high-frequency part obtained before. Finally, the processed IMF results and residual difference are summed up. The results show that after processing, the noise component in the signal is less and the original information is more reserved, which prevents the signal distortion to a great extent and provides more effective data for subsequent processing. In the experiment, the crack defect data collected by the electromagnetic ultrasonic experiment system were processed by the improved EEMD method. Compared with the traditional EEMD method, it can retain the information of crack location more accurately, which proves the effectiveness of the proposed method.

GPS and Acceleration Data in Multimode Trip Data Recognition Based on Wavelet Transform Modulus Maximum Algorithm

The GPS-based travel survey is an emerging data collection method in transportation planning. The survey's application in trip mode detection has been explored in many studies. Most research on trip mode detection methods based on GPS data has been developed and tested with data collected from European and American countries. The methods cannot be easily adapted to Asian countries such as China, India, and Japan, which have much higher population densities, more complex road networks, and highly mixed travel modes during daily commuting. Furthermore, for trip segment division in multimode travel, existing algorithms use travel time and distance thresholds that are highly dependent on local travel behavior and lack universality across traffic environments. This paper proposes an innovative framework for detecting trip modes in complex urban environments. First, a smartphone application, GPSurvey, was developed to collect passive GPS trace data. Then a wavelet transform modulus maximum algorithm was developed for trip segment division. The algorithm has outstanding capabilities for identifying singularity features of a signal; this factor suits the task of detecting mode changes in a complex traffic environment. A neural network module was developed for mode detection on the basis of cell phone GPS location and acceleration data. The results indicate that the proposed method has promising performance. The average absolute detection error of mode transfer time was within 1 min, and the accuracy for detecting all modes was greater than 85%.

Identification of Surface Crack in Steel Rods Using Wavelet Transform Modulus Maximum on Vibration Mode

The traditional identification method of surface crack has some shortcomings such as detection equipment complex, high requirements for operator and not suitable for large-scale structure inspection, vibration method is applied to identify surface crack in steel rods in this paper, using the method of calculating modal analysis combined with time-frequency wavelet analysis. The research on damage identification is performed on steel rods with different deep crack. The research results show that the accurate damage location can be judged by wavelet modulus maximum and the quantitative analysis of single crack identification can be achieved by constructing mathematical expressions of a single crack damage degree and damage index Lipschitz index. The research would province some guidance for engineering applications.

Study on White Noise Suppression Using Complex Wavelet Threshold Algorithm

The complex wavelet transform modulus maximum of the PD signal increases with scale, while the complex wavelet transform modulus maximum of white noise decreases with scale. According to the characteristics, a study on white noise suppression using the effective complex wavelet coefficient (ECWC) threshold method is launched in this paper and a comparison is conducted with the wavelet threshold denoising method of threshold selection of Stein unbiased risk estimate theory and threshold selection of minimax theory. The PD signal denoising results show that ECWC threshold method is more effective and the distortion of the extract PD signal is lower compared with the other method.

Fault Line Selection Research on Single Phase Ground Fault of Small Current Grounding System Based on Wavelet Theory

For the problem of single-phase ground fault line selection of small current grounding system, in this paper, according to the wavelet theory advantage processing capabilities of transient mutation signal ,line selection criterion based on wavelet transform modulus maximum principle of singularity detection is proposed . By analyzing the single-phase ground fault waveform of small current grounding system in the neutral point not effective grounding system, steady state and transient characteristics of fault occurs is obtained, building bus with three outlets of 10KV small current grounding system simulation model by using MATLAB software, and verifying the validity though a large number of simulation experiments on the method.

The Application of Dyadic Wavelets Transform Modulus Maximum in the Seismic Signal De-Noising

This article first briefly introduces the characteristic of the wavelets transform ,explains the concrete operations process that the conjugate gradient method restructuring signal, then narrates in detail the modulus maximum value de-noising's algorithm, finally using the dyadic wavelet transform modulus maximum value de-noising method respectively carries the processing on the synthesis and noisy signal and the actual seismic signal, the experimental result indicates that this algorithm makes the good progress, the seismic data quality is enhanced greatly.

Application Study on of Wavelet Analysis during Compression Ruptrue Procedure of Coal Sample

This paper study the acoustic signal characteristics of coal and rock mass under uniaxial compression by the tests of acoustic emission(AE),which obtain the variation of AE counts and energy.Compressing on original data use wavelet analysis,and extracting the characteristics of signal with the wavelet sym-basis,then decomposing and remodeling the wavelet coefficients,the negative singularity degree indicators of adjacent scales can be got.The results showed that the wavelet transform modulus maximum used to identify the negative singularity degree can achieve the position of the frequency changes point of the acoustic signal,which is of certain guiding significance for prediction of the uniaxial compression process.