A method for extracting weak impact signal in NPP based on adaptive Morlet wavelet transform and kurtosis

Cao, Yanlong; Liu, Mingzhou; Yang, Jiangxin; Cao, Yanan; Fu, Weinan

doi:10.1016/j.pnucene.2017.09.015

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…The kurtosis is very sensitive to the drastic changes of the signal. Because the kurtosis has a larger value for the signal with pulse characteristics, it is often used to measure the pulse characteristics of vibration signals (Cao et al, 2018). The formula of kurtosis is as follows…”

Section: Classification Particle Swarm Optimization Methodsmentioning

confidence: 99%

Signal filtering method of variational mode decomposition and Euclidean distance based on optimizing parameters of classification particle swarm optimization algorithm

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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In order to deal with the problem that the noise of leakage signals from natural gas pipelines has a great influence on the feature extraction of pipeline leakage, this paper proposes a signal denoising method of variational mode decomposition (VMD) and Euclidean distance (ED) based on optimizing parameters of classification particle swarm optimization (CPSO) algorithm. First, CPSO algorithm is used to optimize parameters K and [Formula: see text] of VMD, adaptively. The sum of the ratio of the mean and variance of the cross-correlation coefficient and the ratio of the mean and variance of kurtosis is used as the fitness function of CPSO. Then, the optimized VMD is used to decompose the signal to obtain several intrinsic mode functions (IMFs). Finally, ED is used to select the effective modes, and the signal is reconstructed to achieve signal noise reduction. The corresponding evaluation indicators show that the accuracy and robustness of the improved method are better than other noise reduction methods. The denoising effect is significant, which proves that the algorithm proposed in this paper is effective in signal filtering.

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Section: Classification Particle Swarm Optimization Methodsmentioning

confidence: 99%

Signal filtering method of variational mode decomposition and Euclidean distance based on optimizing parameters of classification particle swarm optimization algorithm

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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“…The dimensionless parameter is not sensitive to the change of data amplitude and frequency which cannot reflect the operational states of the equipment [27]. Kurtosis can characterize the steepness of the peak of the probability density function of the data, and thus can characterize the size of the impact component in the monitoring data [28].…”

Section: Dividing the Work Condition Of Gas Path System Based On Domain Featurementioning

confidence: 99%

“…where, d η is the damping coefficient, E N(0, I) is the expected value of N(0, I) , I is the identity matrix, a η is the parameter of the evolution path b g η , b η can be obtained by Equation (28).…”

Section: Parameter Optimization Based On Covariance Matrix Adaptive Evolution Strategy (Cma-es) Optimization Algorithmmentioning

confidence: 99%

Health State Prediction of Aero-Engine Gas Path System Considering Multiple Working Conditions Based on Time Domain Analysis and Belief Rule Base

et al. 2021

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The gas path system is an important part of an aero-engine, whose health states can affect the security of the airplane. During the process of aircraft operation, the gas path system will have different working conditions over time, owing to the change of control parameters. However, the different working conditions which change the symmetry of the system will affect parameters of the health state prediction model for the gas path system. The symmetry of the system will also change. Therefore, it is important to consider the influence of variable working conditions when predicting the health states of gas path system. The accuracy of the health state prediction results of the gas path system will be low if the same evaluation standard is used for different working conditions. In addition, the monitoring data of the gas path system’s health state feature quantity is huge while the fault data which can reflect the health states of the gas path system are poor. Thus, it is difficult to establish a health state prediction model only by using the monitoring data of the gas path system. In order to avoid problems, this paper proposes a health state prediction model considering multiple working conditions based on time domain analysis and a belief rule base. First, working condition is divided by using time domain characteristics. Then, a belief rule base (BRB) theory-based health state prediction model is built, which can fuse expert knowledge and fault monitoring data to improve modeling accuracy. The reference value of the feature is given by the fuzzy C-means algorithm in a model. To decrease the uncertainty of expert knowledge, the covariance matrix adaptive evolution strategy (CMA-ES) is used as the optimization algorithm. Finally, a NASA public dataset without labels is used to verify the proposed health state model. The results show that the proposed health prediction model of a gas path system can accurately realize health state prediction under multiple working conditions.

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“…Therefore, the Cmor3-3, Cmor5-5, and Cmor7-7 wavelets could be used for the extraction of the nonlinear parameter in ultrasonic signals. The reason for these three wavelet functions having obvious advantages is that these are single-frequency complex sinusoidal functions under a Gaussian envelope, which have a better local focus in both the time and frequency domains, and its waveforms are similar to the analyzed signal [24,25].…”

Section: Factors Influencing Dwfpmentioning

confidence: 99%

Preliminary Research on the Nonlinear Ultrasonic Detection of the Porosity of Porous Material Based on Dynamic Wavelet Fingerprint Technology

Wang

Xie

et al. 2019

Sensors

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Porosity is an important characteristic of porous material, which affects mechanical and material properties. In order to solve the problem that the large distribution range of pore size of porous materials leads to the large detection errors of porosity, the non-linear ultrasonic testing technique is applied. A graphite composite was used as the experimental object in the study. As the accuracy of porosity is directly related with feature extraction, the dynamic wavelet fingerprint (DWFP) technology was utilized to extract the feature parameter of the ultrasonic signals. The effects of the wavelet function, scale factor, and white slice ratio on the extraction of the nonlinear feature are discussed. The SEM photos were conducted using gray value to identify the aperture. The relationship between pore diameter and detection accuracy was studied. Its results show that the DWFP technology could identify the second harmonic component well, and the extracted nonlinear feature could be used for the quantitative trait of porosity. The larger the proportion of the small diameter holes and the smaller the aperture distribution range was, the smaller the error was. This preliminary research aimed to improve the nondestructive testing accuracy of porosity and it is beneficial to the application of porous material in the manufacturing field.

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A method for extracting weak impact signal in NPP based on adaptive Morlet wavelet transform and kurtosis

Cited by 14 publications

References 23 publications

Signal filtering method of variational mode decomposition and Euclidean distance based on optimizing parameters of classification particle swarm optimization algorithm

Signal filtering method of variational mode decomposition and Euclidean distance based on optimizing parameters of classification particle swarm optimization algorithm

Health State Prediction of Aero-Engine Gas Path System Considering Multiple Working Conditions Based on Time Domain Analysis and Belief Rule Base

Preliminary Research on the Nonlinear Ultrasonic Detection of the Porosity of Porous Material Based on Dynamic Wavelet Fingerprint Technology

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