Identification of power quality disturbances based on singular value decomposition of S-transform time-frequency matrixes

2006 ◽  
Author(s):  
Shouliang Liu ◽  
Yong Peng ◽  
Xianyong Xiao ◽  
D. Chen
Keyword(s):  
Singular Value Decomposition ◽  
Power Quality ◽  
Singular Value ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbances ◽  
Value Decomposition

scholarly journals Study on the Thick-Walled Pipe Ultrasonic Signal Enhancement of Modified S-Transform and Singular Value Decomposition

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Haichao Cai ◽  
Chunguang Xu ◽  
Shiyuan Zhou ◽  
Hongjuan Yan ◽  
Liu Yang
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Frequency Resolution ◽  
Scanning System ◽  
Frequency Noise ◽  
Echo Signal ◽  
Time Frequency ◽  
S Transform ◽  
Ultrasonic Flaw ◽  
Value Decomposition

When detecting the ultrasonic flaw of thick-walled pipe, the flaw echo signals are often interrupted by scanning system frequency and background noise. In particular when the thick-walled pipe defect is small, echo signal amplitude is often drowned in noise signal and affects the extraction of defect signal and the position determination accuracy. This paper presents the modified S-transform domain singular value decomposition method for the analysis of ultrasonic flaw echo signals. By changing the scale rule of Gaussian window functions with S-transform to improve the time-frequency resolution. And the paper tries to decompose the singular value decomposition of time-frequency matrix after the S-transform to determine the singular entropy of effective echo signal and realize the adaptive filter. Experiments show that, using this method can not only remove high frequency noise but also remove the low frequency noise and improve the signal-to-noise ratio of echo signal.

Power Quality Disturbances Classification Based on Modified S Transform and Decision Tree

2013 ◽  
Vol 860-863 ◽  
pp. 1891-1894
Author(s):  
Ji Liang Yi ◽  
Ou Yang Qin
Keyword(s):  
Decision Tree ◽  
Power Quality ◽  
Statistical Features ◽  
Time Frequency ◽  
S Transform ◽  
Novel Method ◽  
Power Quality Disturbances ◽  
Frequency Properties

A novel method for power quality disturbances classification is presented using modified S transform (MST) and decision tree. The time-frequency properties of power quality disturbances are analyzed and the effects of window-wide parameter g on the properties are discussed. Four statistical features are extracted from the MST module time-frequency matrix and a decision tree is utilized to recognize 9 power quality disturbances. The simulations are made to illustrate the validity of the method proposed for power quality disturbances recognition.

Improved S-Transform for Time-Frequency Analysis for Power Quality Disturbances

2021 ◽  
pp. 1-1
Author(s):  
Chengbin Liang ◽  
Zhaosheng Teng ◽  
Jianmin Li ◽  
Wenxuan Yao ◽  
Lei Wang ◽  
...  
Keyword(s):  
Power Quality ◽  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
S Transform ◽  
Power Quality Disturbances

Seizure detection approach using S-transform and singular value decomposition

2015 ◽  
Vol 52 ◽  
pp. 187-193 ◽  
Author(s):  
Yudan Xia ◽  
Weidong Zhou ◽  
Chengcheng Li ◽  
Qi Yuan ◽  
Shujuan Geng
Keyword(s):  
Singular Value Decomposition ◽  
Seizure Detection ◽  
Singular Value ◽  
S Transform ◽  
Detection Approach ◽  
Value Decomposition

scholarly journals A Tacholess Order Tracking Method Based on Inverse Short Time Fourier Transform and Singular Value Decomposition for Bearing Fault Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6924
Author(s):  
Lang Xu ◽  
Steven Chatterton ◽  
Paolo Pennacchi ◽  
Chang Liu
Keyword(s):  
Fourier Transform ◽  
Singular Value Decomposition ◽  
Rotation Frequency ◽  
Singular Value ◽  
Target Component ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Order Tracking ◽  
Value Decomposition ◽  
Short Time

Order tracking has been widely used to diagnose failures of variable speed rotating machines. The performance of the TOT (Time-Frequency Domain Tacholess Order Tracking) methods is based on the correct separation of the target component strictly related to the shaft rotation frequency. Currently, most of the methods have focused on obtaining the instantaneous frequency with accuracy. In this paper, a new TOT method has been proposed that combines the inverse short-time Fourier transform (ISTFT) with singular value decomposition (SVD). The target component closely related to the shaft rotation frequency is selected and filtered approximately in the time-frequency domain. Hence, the ISTFT is adopted to reverse the target component into the time domain. Next, SVD is used to refine the roughly filtered target component. Finally, the phase of the refined signal is extracted to resample the original signal. The performance of the method was tested using real vibration signals collected from a large-scale test rig of a high-speed train traction system.

Bearing fault diagnosis method based on the generalized S transform time–frequency spectrum de-noised by singular value decomposition

2018 ◽  
Vol 233 (7) ◽  
pp. 2467-2477 ◽  
Author(s):  
Jianhua Cai ◽  
Yongliang Xiao
Keyword(s):  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Singular Values ◽  
Rolling Bearing ◽  
Singular Value ◽  
Time Frequency ◽  
Bearing Fault ◽  
S Transform ◽  
Generalized S Transform ◽  
Value Decomposition

In view of the fact that the random noise interferes with the characteristic extraction of a rolling bearing fault signal, a new method of fault feature extraction is proposed based on the combination of the generalized S transform and singular value decomposition (SVD). Firstly, the 2D time–frequency spectrum bearing fault signal is obtained by applying the generalized S transform, and the time–frequency spectrum matrix is used as the objective matrix of SVD to solve the singular values. Then the K-means clustering algorithm is used to classify the singular value sequence, and the singular values for reconstruction are determined. Finally, the de-noised matrix is carried out the generalized S inversion transform to get the de-noised fault signal, and the power spectrum is calculated to finish the fault diagnosis. By analyzing the simulated signal and the actual bearing fault data, results show that the proposed method can effectively identify typical faults of rolling bearings and improve the diagnosis effect of rolling bearing faults. And it provides a new way to realize the fault diagnosis of rolling bearings under noise.

Feature extraction of GIS partial discharge signal based on S-transform and singular value decomposition

2017 ◽  
Vol 11 (2) ◽  
pp. 186-193 ◽  
Author(s):  
Dangdang Dai ◽  
Xianpei Wang ◽  
Jiachuan Long ◽  
Meng Tian ◽  
Guowei Zhu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Singular Value Decomposition ◽  
Partial Discharge ◽  
Singular Value ◽  
S Transform ◽  
Value Decomposition
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