scholarly journals Simulation Research on High-Speed Railway Dropper Fault Detection and Location Based on Time-Frequency Analysis

2020 ◽  
Vol 1631 ◽  
pp. 012100
Author(s):  
Jin Li ◽  
Xuewu Zhang ◽  
Cheng Zhang ◽  
Tiantian Tian
Keyword(s):  
Fault Detection ◽  
Frequency Analysis ◽  
High Speed ◽  
High Speed Railway ◽  
Simulation Research ◽  
Time Frequency Analysis ◽  
Time Frequency

An Investigation of Feature Extraction of Local Energy in Joint Time-Frequency Analysis Based on Process Systems Modeling and Simulation

2012 ◽  
Vol 198-199 ◽  
pp. 803-807
Author(s):  
Feng Li Wang ◽  
Shu Lin Duan ◽  
Hong Tao Gao
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Local Energy ◽  
Simulation Research ◽  
Time Frequency Analysis ◽  
Process Systems ◽  
Frequency Space ◽  
Time Frequency ◽  
Stationary Signals ◽  
Local Wave

Aiming at the characteristics of local properties of the non-stationary signals, a noval feature extraction approach based on the local energy in joint time-frequency analysis is proposed. The concept of local energy in joint time- frequency analysis based on local wave analysis was used to measure the signal energy in time-frequency space of the signal. Firstly, analyze the signal with local wave method and then make Hilbert transformation of it. Then partition several areas in time frequency space and compute its local energy. From the expression of local wave time-frequency distributing, not only total energy of signal can be computed but also local energy in time-frequency space. Simulation research indicates that the developed approach was effective.

scholarly journals Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4102
Author(s):  
Tomas A. Garcia-Calva ◽  
Daniel Morinigo-Sotelo ◽  
Oscar Duque-Perez ◽  
Arturo Garcia-Perez ◽  
Rene de J. Romero-Troncoso
Keyword(s):  
Fault Detection ◽  
Frequency Analysis ◽  
Induction Motors ◽  
Spectral Estimation ◽  
Diagnostic Techniques ◽  
Frequency Resolution ◽  
Minimum Norm ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Conditions

In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.

scholarly journals Multiple-fault detection in water pipelines using transient-based time-frequency analysis

2016 ◽  
Vol 18 (6) ◽  
pp. 975-989 ◽  
Author(s):  
Jilong Sun ◽  
Ronghe Wang ◽  
Huan-Feng Duan
Keyword(s):  
Fault Detection ◽  
Frequency Analysis ◽  
Preliminary Test ◽  
Detection Methods ◽  
Pipeline System ◽  
Transient Pressure ◽  
Multiple Fault ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Water Pipelines

Pipe faults, such as leakage and blockage, commonly exist in water pipeline systems. It is essential to identify and fix these failures appropriately in order to reduce the risk of water pollution and enhance the security of water supply. Recently, transient-based detection methods have been developed for their advantages of non-intrusion, efficiency and economics compared to traditional methods. However, this method is so far limited mainly to simple pipelines with a single known type of pipe fault in the system. This paper aims to extend the transient-based method to multiple-fault detection in water pipelines. For this purpose, this study introduced an efficient and robust method for transient pressure signal analysis – a combination of the empirical mode decomposition and Hilbert transform – in order to better identify and detect different anomalies (leakage, blockage and junction) in pipelines. To validate the proposed transient-based time-frequency analysis method, laboratory experimental tests were conducted in this study for a simple pipeline system with multiple unknown types of pipe faults including leakages, blockages and junctions. The preliminary test results and analysis indicate that multiple pipe faults in simple pipelines can be efficiently identified and accurately located by the proposed method.

Modal Analysis of a Bridge During High-Speed Train Passages by Enhanced Variational Mode Decomposition

2020 ◽  
Vol 20 (13) ◽  
pp. 2041002
Author(s):  
Xiao-Mei Yang ◽  
Chun-Xu Qu ◽  
Ting-Hua Yi ◽  
Hong-Nan Li ◽  
Hua Liu
Keyword(s):  
Energy Distribution ◽  
Modal Analysis ◽  
Frequency Analysis ◽  
High Speed ◽  
Large Scale ◽  
Variational Mode Decomposition ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Variations

Modal analysis of bridge under high-speed trains is essential to the design and health monitoring of bridge, but it is difficult to be implemented since the vehicle–bridge interaction (VBI) effect is involved. In this paper, the time–frequency analysis technique is performed on the non-stationary train-induced bridge responses to estimate the frequency variations. To suppress the interference terms in time–frequency analysis but preserve the time-variant characteristics of responses, the enhanced variational mode decomposition (VMD) is proposed, which is used to decompose the train-induced dynamic response into many of envelope-normalized intrinsic mode functions (IMFs). Then the short-time Fourier transform is applied to observe the time–frequency energy distribution of each IMF. The train-induced bridge signals measured from a large-scale high-speed railway bridge are analyzed in this paper. The IMFs associated with the pseudo-frequencies caused by train or the resonant frequencies of bridge are distinguished. And, frequency variations are captured from the time–frequency energy distributions of envelope-normalized IMFs. The results show the proposed method can extract the frequency variations of low-energy IMFs effectively, which are hard to be observed from the time–frequency energy distribution of train-induced bridge response. The instantaneous frequency characteristics extracted from the train-induced bridge response could be the important support for investigating the VBI effect of train–bridge system.

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