Study on the natural gas pipeline safety monitoring technique and the time-frequency signal analysis method

2017 ◽  
Vol 47 ◽  
pp. 1-9 ◽  
Author(s):  
Zhigang Qu ◽  
Yanfen Wang ◽  
Huanhuan Yue ◽  
Yang An ◽  
Liqun Wu ◽  
...  
Keyword(s):  
Natural Gas ◽  
Signal Analysis ◽  
Safety Monitoring ◽  
Analysis Method ◽  
Frequency Signal ◽  
Time Frequency ◽  
Natural Gas Pipeline ◽  
Monitoring Technique ◽  
Pipeline Safety ◽  
Signal Analysis Method

A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

2011 ◽  
Vol 204-210 ◽  
pp. 973-978
Author(s):  
Qiang Guo ◽  
Ya Jun Li ◽  
Chang Hong Wang
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Low Snr ◽  
The Time Domain ◽  
Signal Analysis Method

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

Compensation of sound velocity variation based on resampling algorithm for natural gas pipeline safety monitoring

Measurement ◽  
2019 ◽  
Vol 148 ◽  
pp. 106942
Author(s):  
Yang An ◽  
Xiaocen Wang ◽  
Bin Yue ◽  
Zhigang Qu ◽  
Liqun Wu ◽  
...  
Keyword(s):  
Natural Gas ◽  
Sound Velocity ◽  
Safety Monitoring ◽  
Gas Pipeline ◽  
Velocity Variation ◽  
Natural Gas Pipeline ◽  
Pipeline Safety

Evaluating Risk and Natural Gas Pipeline Safety

2017 ◽  
Vol 45 (4) ◽  
pp. 657-680 ◽  
Author(s):  
Michael James Brogan
Keyword(s):  
Natural Gas ◽  
Gas Pipeline ◽  
Natural Gas Pipeline ◽  
Pipeline Safety

Research on Applied-Information Technology in Digital Speech Based on LMD Algorithm

2014 ◽  
Vol 1014 ◽  
pp. 447-451
Author(s):  
Dong Kang He ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Production Function ◽  
Signal Analysis ◽  
Training Model ◽  
Recognition System ◽  
Analysis Method ◽  
End Effects ◽  
Local Mean ◽  
Non Stationary Signal ◽  
Signal Analysis Method

As a new nonlinear and non-stationary signal analysis method,local mean decomposition (LMD) has a good adaptability. We decompose the original non-stationary acceleration vibration signals into several stationary production function (PF).But performing LMD will produce end effects which make results distorted. A hidden Markov model (HMM)-based speech recognition system for Chinese spell.After analyzing reasons for end effects of LMD in detail,a new method based on weighted matching similar waveform was proposed.Experiments in speech recognition to the production function as the training model, the more traditional identification method to identify higher rates. LMD is an effective method. It is feasible to extract the feature from speech signals with LMD.

scholarly journals A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang
Keyword(s):  
Time Series ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Forecast Model ◽  
Practical Significance ◽  
Frequency Signal ◽  
Time Frequency ◽  
Simple Basis ◽  
Best Fitting ◽  
Track Irregularity

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

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