scholarly journals Compressed-Sensing-Based Time–Frequency Representation for Disturbance Characterization of Maglev On-Board Distribution Systems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1909
Author(s):  
Lu Xing ◽  
Yinghong Wen ◽  
Shi Xiao ◽  
Jinbao Zhang ◽  
Dan Zhang
Keyword(s):  
Compressed Sensing ◽  
Distribution System ◽  
Distribution Systems ◽  
Low Frequency ◽  
Time Varying ◽  
L1 Norm ◽  
Time Frequency ◽  
Frequency Representation ◽  
Optimal Kernel ◽  
Conducted Emissions

The frequency variating source, linear generator, and switching devices lead to dynamic characteristics of the low-frequency conducted emissions within maglev on-board distribution systems. To track the time-varying feature of these disturbances, a joint time–frequency representation combined adaptive optimal kernel with compressed sensing technique is proposed in this paper. The joint representation is based on Wigner–Ville distribution, and employs adaptive optimal kernel to remove undesirable cross terms. The compressed sensing technique is introduced to deal with the tradeoff between cross-component reduction and auto-component smearing faced by kernel-function-based bilinear time–frequency representation. The time–frequency aggregation and accuracy performance of joint time–frequency representation is quantified using Rényi entropy and l1-norm. To verify its performance in disturbance signature analysis for distribution systems and primarily characterize the low-frequency conducted emissions of maglev, a maglev on-board distribution system experimental platform is employed to extract the low-frequency disturbances which pose threats to the controlling system. Comparison with Wigner–Ville distribution demonstrates the joint time–frequency representation method outperforms in tracking time-varying and transient disturbances of maglev on-board distribution systems.

scholarly journals Fault Detection for the Scraper Chain Based on Vibration Analysis Using the Adaptive Optimal Kernel Time-Frequency Representation

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xing Zhang ◽  
Wei Li ◽  
Zhencai Zhu ◽  
Shanguo Yang ◽  
Fan Jiang
Keyword(s):  
Fault Detection ◽  
Vibration Analysis ◽  
Large Scale ◽  
Experimental Testing ◽  
Operating Conditions ◽  
Mining Equipment ◽  
Time Frequency ◽  
Failure Patterns ◽  
Frequency Representation ◽  
Optimal Kernel

A scraper conveyor is a key component of large-scale mechanized coal mining equipment, and its failure patterns are mainly caused by chain jam and chain fracture. Due to the difficulties with direct measurement for multiple performance parameters of the scraper chain, this paper deals with a novel strategy for fault detection of the scraper chain based on vibration analysis of the chute. First, a chute vibration model (CVM) is applied for modal analysis, and the hammer impact test (HIT) is conducted to validate the accuracy of the CVM; second, the measuring points for vibration analysis of the chute are determined based on the modal assurance criterion (MAC); and third, to simulate the actual vibration properties of the chute, a dynamic transmission system model (DTSM) is constructed based on finite element modeling. The fixed-point experimental testing (FPET) is then conducted to indicate the correctness of simulation results. Subsequently, the DTSM-based vibration responses of the chute under different operating conditions are obtained. In this paper, the proposed strategy is employed to determine the occurrence of chain faults by amplitude comparisons, while failure patterns are distinguished by the adaptive optimal kernel time-frequency representation (AOKR).

Applied Research on Segmented-Hilbert-Huang Transform Algorithm in Fault Feature Extraction of Main Reducer

2013 ◽  
Vol 823 ◽  
pp. 417-421 ◽  
Author(s):  
Feng Yun Huang ◽  
Huan Huan Sun ◽  
Hao Pan ◽  
Wei Ru Zhang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Vibration Signal ◽  
Model Decomposition ◽  
Frequency Dictionary ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Frequency Representation ◽  
Scale Characteristics ◽  
Frequency Impulse

For the multi-time scale characteristics of vibration signal, a composite multi-frequency dictionary combining the low-frequency Fourier dictionary and the high-frequency impulse time-frequency dictionary is constituted, to decompose multi-component vibration signal into the combination of several one-component signals. The use of empirical model decomposition (EDM) in high-frequency impulse Component signal including feature information is to realize segmented Hilbert-Huang transform of signal and to acquire the time-frequency representation of every one-component signal, which is the process of fault information extraction of vibration signal. The application of the method in main reducer fault diagnosis verifies the engineering practicability and validity of the new algorithm.

Adaptive optimal‐kernel time‐frequency representation and its application in characterizing seismic attenuation

2011 ◽  
Author(s):  
Xiaokai Wang ◽  
Jinghuai Gao ◽  
Wenchao Chen ◽  
Jin Xu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Seismic Attenuation ◽  
Time Frequency ◽  
Frequency Representation ◽  
Optimal Kernel

An Improved of Multiple Harmonic Sources Identification in Distribution System with Inverter Loads by Using Spectrogram

2016 ◽  
Vol 7 (4) ◽  
pp. 1355
Author(s):  
M.H. Jopri ◽  
A.R. Abdullah ◽  
M. Manap ◽  
M.R. Yusoff ◽  
T. Sutikno ◽  
...  
Keyword(s):  
Significant Relationship ◽  
Distribution System ◽  
Correct Identification ◽  
Time Frequency ◽  
Sources Identification ◽  
Frequency Representation ◽  
Cost Efficient ◽  
Relationship Of ◽  
Accuracy Speed ◽  
Time Frequency Distribution

This paper introduces an improved of multiple harmonic sources identification that been produced by inverter loads in power system using time-frequency distribution (TFD) analysis which is spectrogram.  The spectrogram is a very applicable method to represent signals in time-frequency representation (TFR) and the main advantages of spectrogram are the accuracy, speed of the algorithm and use low memory size such that it can be computed rapidly. The identification of multiple harmonic sources is based on the significant relationship of spectral impedances which are the fundamental impedance (Z1) and harmonic impedance (Zh) that extracted from TFR. To verify the accuracy of the proposed method, MATLAB simulations carried out several unique cases with different harmonic producing loads on IEEE 4-bus test feeder cases. It is proven that the proposed method is superior with 100% correct identification of multiple harmonic sources. It is envisioned that the method is very accurate, fast and cost efficient to localize harmonic sources in distribution system.

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