The EMD Method and Its Application to Signal Processing for Infrared Gas Detection

2011 ◽  
Vol 30 (10) ◽  
pp. 2516-2519 ◽  
Author(s):  
Qiong Xie ◽  
Jian-ping Li ◽  
Xiao-guang Gao ◽  
Jian Jia
Keyword(s):  
Signal Processing ◽  
Gas Detection ◽  
Emd Method

The Research on the Methodology of Diagnosing the Fault of Bearing in Warships Based on NS-EMD

2013 ◽  
Vol 711 ◽  
pp. 352-357
Author(s):  
Jin E Huang ◽  
Dong Xu ◽  
Yan Lei Wang ◽  
Yang Zhang ◽  
Shuang Wang
Keyword(s):  
Signal Processing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Processing Method ◽  
Image Method ◽  
Vibration Source ◽  
Rolling Bearings ◽  
Average Value ◽  
Signal Processing Method ◽  
Emd Method

EMD is now a commonly used nonlinear and instable signal processing method, but it has boundary runaway and modal aliasing. The single disunited IMF cannot well reflect the characteristics of the respective vibration source. Therefore, in order to suppress the boundary runaway that will appear in the process of EMD, the image method is used to extend the length of signal data. To solve the modal aliasing, it is necessary to decompose the extended data by the EMD method, to distinguish the IMF that produces modal aliasing after decomposition, to integrate it according to the integrity of the EMD and then to re-decompose it after adding broadband white noise with the average value of zero. On the basis of that, it is better to improve NS-EMD method and realize the AM-FM demodulation by standardized method. By the spectrum analysis, we extract the fault characteristics of rolling bearings and propose a method to diagnose faults of rolling bearing. The results of analyzing the simulation and the vibration signal of fault rolling bearing shows that the method can effectively extract fault characteristics of rolling bearing.

Application of EMD method to friction signal processing

2008 ◽  
Vol 22 (1) ◽  
pp. 248-259 ◽  
Author(s):  
Kejian Guo ◽  
Xingang Zhang ◽  
Hongguang Li ◽  
Guang Meng
Keyword(s):  
Signal Processing ◽  
Emd Method

An Improved EMD Method and its Application in Nonstationary Signals Analysis

2012 ◽  
Vol 429 ◽  
pp. 313-317 ◽  
Author(s):  
Jian Hui Chen
Keyword(s):  
Signal Processing ◽  
Integrated Circuit ◽  
Large Scale ◽  
Rapid Development ◽  
Difficult Problem ◽  
Energy Concentration ◽  
Joint Analysis ◽  
Nonstationary Signals ◽  
Time Frequency ◽  
Emd Method

Empirical mode decomposition (EMD) method based on HHT has exhibited unique advantages such as adaptability and highly efficiency in many nonlinear, nonstationary signals processing applications. It breaks the uncertainty principle limit, but the traditional EMD still has its deficiencies. In this article, we construct a new wavelet which has excellent decomposing-frequency performance and energy concentration, and then an improved EMD method based on this wavelet is presented. Results of numerical simulation show the validity and efficiency of the method proposed in paper are better than traditional one. Furthermore, some foreseeable trends of time-frequency distribution technologies are described. The systems in reality, strictly speaking, tend to non-linear, so most practical signals are non-stationary random signals. Nonlinear, nonstationary signals analysis is a very significant and difficult problem in almost all technical fields such as automation, communication, aerospace- engineering, biomedicine, structural fault diagnosis and so on. Owed to the rapid development of large scale integrated circuit technology and artificial intelligence, the exploration of signal processing theories have got a sharply impetus. A series of new modern signal processing theories and methods have appeared to meet the need of time-frequency joint analysis of nonlinear, non-Gaussian and non-stationary signals, including discrete short-time Fourier transform, wavelet transform, Hilbert-Huang transform and so on. Time-frequency joint analysis can observe the evolution of the signal in the time domain and the frequency domain simultaneously, provide local time-frequency characteristics of the signal.

The Signal Processing in Track Irregularity Detection

2013 ◽  
Vol 427-429 ◽  
pp. 1574-1578
Author(s):  
Xin Chang Liu ◽  
Qi Zhen Jiang ◽  
Xiao Dong Chai ◽  
Shu Bin Zheng ◽  
Li Ming Li
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Detection Accuracy ◽  
Mean Square ◽  
Sampling System ◽  
Time Domain Signal ◽  
Track Irregularity ◽  
Signal Use ◽  
Emd Method ◽  
Track Irregularities

In order to overcome the non-uniform property of time domain signal which is obtained in the process of track irregularity detection, design a set of equidistant signal sampling system to collect signal. Use the EMD method based on the criterion of continuous mean square error de-nosing. By integrating the signal get the space trajectory of vector. Experiments show that the track irregularities detection accuracy has been greatly improved.

Rotor Complex Fault Vibration Analysis Based on Frequency Heterodyne EMD Method

2011 ◽  
Vol 199-200 ◽  
pp. 845-849 ◽  
Author(s):  
Hong Ying Hu ◽  
Er Bao ◽  
Jing Kang
Keyword(s):  
Signal Processing ◽  
Processing Method ◽  
Frequency Resolution ◽  
Vibration Signals ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Complex Fault ◽  
Non Stationary Signal ◽  
Signal Processing Methods ◽  
Emd Method

Rotor Complex Fault Vibration signals are very hard to analysis since there are many frequencies lies in them. It needs new signal processing methods to deal with these problems. Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. The frequency heterodyne EMD method can improve the frequency resolution of EMD by shifting the original frequencies to enlarge the frequencies ratio between components. It proves that the method can enhance the performance of EMD easily and effectively. The paper discusses the principle and steps of this method in detail and uses it to analyse rotor complex fault signals. The result shows that frequency heterodyne EMD method can separate different faults and detect the weaker faults in complex fault more effectively than that of normal EMD method.

A Gas Detection System Based on the Micro Light Splitting Principle

2012 ◽  
Vol 542-543 ◽  
pp. 57-65 ◽  
Author(s):  
Hai Rong Wang ◽  
Wei Zhang ◽  
Di Cen ◽  
Min Tian
Keyword(s):  
Carbon Monoxide ◽  
Signal Processing ◽  
Infrared Absorption ◽  
Detection System ◽  
Maximum Error ◽  
Coal Industry ◽  
Experimental Results ◽  
Gas Detection ◽  
Schematic Diagram ◽  
Blazed Grating

This paper presents a novel system for detecting concentration of the CH4 and CO gases, which exist widely in the coal industry. The system applies micro blazed grating to split the collimated light and may get the infrared absorption light with certain wavelength and bandwidth for the gases detection. The schematic diagram, calculation, signal processing, and setup were described in details. Experimental results indicate that the detection system has the ability to detect methane and carbon monoxide with maximum error of 0.169% and 58.3ppm, stability of 0.76% and 0.22%, respectively.

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