Time-frequency analysis and detecting method research on milling force token signal in spindle current signal

2009 ◽  
Vol 52 (10) ◽  
pp. 2810-2813 ◽  
Author(s):  
XinYong Mao ◽  
HongQi Liu ◽  
Bin Li
Keyword(s):  
Frequency Analysis ◽  
Current Signal ◽  
Milling Force ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Detecting Method ◽  
Spindle Current

Current-Aided Time-Frequency Analysis of Vibration Signals for Gearbox Fault Diagnosis

2018 ◽  
Author(s):  
Xiaotong Tu ◽  
Yue Hu ◽  
Fucai Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Current Signal ◽  
Reliable Technique ◽  
Time Frequency Analysis ◽  
Rotating Speed ◽  
Planetary Gearbox ◽  
Time Frequency

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.

An Experimental Study on Working State Recognition of Machine Tools

2012 ◽  
Vol 201-202 ◽  
pp. 707-710
Author(s):  
Teng Fei Fang ◽  
Guo Fu Li ◽  
Lei Wang ◽  
Hong Bin Li ◽  
Wei Guo
Keyword(s):  
Frequency Analysis ◽  
Machine Tools ◽  
Wavelet Packet ◽  
Current Signal ◽  
Wavelet Packet Analysis ◽  
Time Frequency Analysis ◽  
State Recognition ◽  
Time Frequency ◽  
Different Characteristics ◽  
One Machine

In order to obtain the real-time working state of machine tools, this experiment extracted the characteristics of machine tools using joint time-frequency analysis and wavelet packet analysis for the total current signal collected, to distinguish which machine is running. First, use joint time-frequency analysis on signal of a single machine to get different characteristics. And find some frequency points with amplitude changing significantly, preparing for the subsequent experiment. Then use wavelet packet analysis on the total signal of more than one machine, finding more obvious characteristics of the different machines with different speeds. Thus it is easy to identify which machine is working. By this experiment, we can save labor, improve efficiency and integrate information in system conveniently.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency

Anti-Radiation-Missile detection based on parameterized time-frequency analysis

2010 ◽  
Vol 30 (11) ◽  
pp. 3108-3110
Author(s):  
Xiao-ming LIU ◽  
Jian-dong WANG ◽  
Xu-dong WANG
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Time Frequency
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