Wavelet Transform Adaptive De-noising Algorithm and Application Based on a Novel Variable Step Function

2009 ◽  
Author(s):  
Jingjing Jin ◽  
Xu Wang ◽  
Shilong Li ◽  
Yingnan Wu
Keyword(s):  
Wavelet Transform ◽  
Step Function ◽  
Variable Step

Adaptive Filter with Multiple-Scale Decomposition Rotor Broken Bars in Induction Fault Diagnosis

2013 ◽  
Vol 273 ◽  
pp. 428-433
Author(s):  
De En ◽  
Xiao Long Shi ◽  
Huang He Wei ◽  
Na Na Wei ◽  
Chang Sheng Zhou
Keyword(s):  
Wavelet Transform ◽  
Adaptive Filtering ◽  
Multiple Scale ◽  
Frequency Component ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Broken Rotor Bar ◽  
Stator Current ◽  
Variable Step

There is a very small additional current component of frequency in the stator current signal when motor has broken rotor bars.So adaptive notch filter is applied to process the signals of the stator current in induction motors.The variable step size LMS algorithm and the multiple-scale wavelet transform are merged into the adaptive filtering system.A method is proposed, that is a LMS adaptive filtering algorithm with modified variable step size based on multiple-scale wavelet transform(MSWT-MVSS-LMS).It can eliminate interference from power frequency component to frequency component of broken rotor bar and achieve precise identification to frequency component of broken rotor bar from FFT.The result is a great help to extract the feature component of rotor fault and improve the sensitivity of fault diagnosis.The simulation show that the method is valid and effective.

scholarly journals The Variable Step-Size Wavelet Transform-Domain LMS Adaptive Filter Algorithm

2018 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Mohammad Shams Esfand Abadi ◽  
Hamid Mesgarani ◽  
Seyed Mahmoud Khademiyan
Keyword(s):  
Wavelet Transform ◽  
Adaptive Filter ◽  
Variable Step Size ◽  
Transform Domain ◽  
Step Size ◽  
Variable Step

Variable Step-Size Wavelet Vector Machine Blind Equalization Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4892-4896
Author(s):  
Ye Cai Guo ◽  
Zhi Chao Zhang ◽  
Fang Xu ◽  
Shi Jie Guo
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Convergence Rate ◽  
Blind Equalization ◽  
Weight Vector ◽  
Support Vector ◽  
Variable Step Size ◽  
Step Size ◽  
Equalization Algorithm ◽  
Variable Step

In order to overcome the contradiction of the CMA with a constant step-size between the convergence rate and the residual mean square error(MSE), on the basis of analyzing the idea of variable step-size, the feature of Support Vector Machine(SVM) and Wavelet Transform, a Variable step-size Wavelet transform Support vector machine Constant Modulus blind equalization Algorithm (VWSCMA) is proposed. In the proposed algorithm, the variable step-size is used to solve the contradiction between the convergence rate and the residual MSE, SVM is employed to optimize the weight vector of equalizer, and wavelet transform is used to reduce the autocorrelation of input signals of equalizer. Simulation results show that the proposed algorithm can effectively overcome the contradiction between the convergence rate and the residual error and has good equalization performance.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

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