Low-pass digital filter with amplitude 1/f2 attenuation based empirical mode decomposition of vortex signal processing method

2021 ◽  
Vol 92 (4) ◽  
pp. 045002
Author(s):  
Jie Chen ◽  
Nan Zhang ◽  
Bin Li
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Digital Filter ◽  
Processing Method ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Low Pass

Signal Processing Method Based on Empirical Mode Decomposition in the SO2Concentration Monitoring

2014 ◽  
Vol 43 (2) ◽  
pp. 228002
Author(s):  
王书涛 WANG Shu-tao ◽  
李梅梅 LI Mei-mei ◽  
李盼 LI Pan ◽  
刘铭华 LIU Ming-hua ◽  
王丽媛 WANG Li-yuan ◽  
...  
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Processing Method ◽  
Signal Processing Method ◽  
Mode Decomposition

scholarly journals Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

2019 ◽  
Vol 13 (1) ◽  
pp. 4477-4492
Author(s):  
M. Firdaus Isham ◽  
M. Salman Leong ◽  
L. M. Hee ◽  
Z. A. B. Ahmad
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Processing Method ◽  
Mode Number ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Learning Machine

Vibration-based monitoring and diagnosis provide an excellent and reliable monitoring strategies for maintaining and sustaining a million dollars of industrial assets. The signal processing method is one of the key elements in gearbox fault diagnosis for extracting most useful information from raw vibration signals. Variational mode decomposition (VMD) is one of the recent signal processing methods that helps to solve many limitations in traditional signal processing method. However, pre-determine the input parameters especially the mode number become a challenging task for using this method. Then, this study aims to propose an iterative approach for selecting the mode number for the VMD method by using the normalized mean value (NMV) plot. The NMV value is calculates based on the ratio of a summation of VMD modes and the input signals. The result shows that the proposed iterative VMD approach can select an accurate mode number for the VMD method. Then, the vibration signals decomposed into different VMD modes and used for gearbox fault diagnosis. Statistical features have been extracted from the selected VMD modes and pass into extreme learning machine (ELM) for fault classification. Iterative VMD-ELM provide significance improvement of about 20% higher accuracy in classification result as compared with EMD-ELM. Hence, this research study offers a new mean for gearbox diagnosis strategy.  

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.

'Straightening through smoothings' seen as a digital filter

1976 ◽  
Vol 54 (13) ◽  
pp. 1309-1312 ◽  
Author(s):  
P. Marchand ◽  
P. Veillette
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Filter ◽  
Digital Signal ◽  
Processing Method ◽  
End Effect ◽  
Signal Processing Method

It is shown that the digital signal processing method called 'straightening through smoothings' is exactly equivalent to a digital filter, even including end-effect corrections.

A New Method for Biomedical Signal Processing with EMD and ICA Approach

2012 ◽  
Vol 546-547 ◽  
pp. 548-552
Author(s):  
Wei Yu ◽  
Qiang Han ◽  
Jing Jing Ma ◽  
Pei Xie
Keyword(s):  
Signal Processing ◽  
Processing Method ◽  
Biomedical Signal Processing ◽  
Signal Extraction ◽  
Fetal Electrocardiogram ◽  
Biomedical Signal ◽  
Signal Processing Method ◽  
Mode Decomposition ◽  
Novel Method ◽  
Difficult Issue

Faint signal extraction is always a difficult issue in biomedical signal processing field, because the desired signal is often submerged in several relatively large signals or noises. A novel faint signal processing method based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is developed to enhance the sensitivity and reliability of faint signal detection. This novel method includes two major steps, which is, firstly the decomposition of the biomedical composite signal using EMD, then the classification or extraction of the desired faint signal component through ICA. This paper explored the working principles and the performance of this novel signal processing method under the specific biomedical environment of fetal electrocardiogram extraction (FECG). The experimental results show that the proposed method has better extraction effect and quality compared with traditional ICA methods.

Sign in / Sign up

Export Citation Format

Share Document