AdSR based fault diagnosis for three-axis boring and milling machine

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LI, Bing .
AdSR based fault diagnosis for three-axis boring and milling machine. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 58, n.9, p. 527-533, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/adsr-based-fault-diagnosis-for-three-axis-boring-and-milling-machine/>. Date accessed: 21 feb. 2020. 
doi:http://dx.doi.org/10.5545/sv-jme.2011.272.
Li, B.
(2012).
AdSR based fault diagnosis for three-axis boring and milling machine.
Strojniški vestnik - Journal of Mechanical Engineering, 58(9), 527-533.
doi:http://dx.doi.org/10.5545/sv-jme.2011.272
@article{sv-jmesv-jme.2011.272,
	author = {Bing  Li},
	title = {AdSR based fault diagnosis for three-axis boring and milling machine},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {58},
	number = {9},
	year = {2012},
	keywords = {stochastic resonance; fault diagnosis;boring and milling machine},
	abstract = {This paper introduced an adaptive stochastic resonance (AdSR) signal processing technique to extract fault feature of machining accuracy decay in boring and milling machine providing a vibration time-frequency distribution with adaptable precision. The AdSR uses correlation coefficient of the input signals and noise as a weight to construct the weighted kurtosis (WK) index. The influence of high frequency noise is alleviated and the index used in traditional SR is improved accordingly. The AdSR with WK can obtain optimal parameters adaptively. In addition, through the secondary utilization of noise, AdSR makes the signal output waveform smoother and the fluctuation period more obvious. It has been found that AdSR appears to be a better tool compared to fast Fourier transform for fault characterization extraction in boring and milling machine in experiment case. It was concluded that AdSR based signal processing technology successfully diagnosis the fault of machining accuracy decay in three-axis boring and milling machine.},
	issn = {0039-2480},	pages = {527-533},	doi = {10.5545/sv-jme.2011.272},
	url = {https://www.sv-jme.eu/article/adsr-based-fault-diagnosis-for-three-axis-boring-and-milling-machine/}
}
Li, B.
2012 June 58. AdSR based fault diagnosis for three-axis boring and milling machine. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 58:9
%A Li, Bing 
%D 2012
%T AdSR based fault diagnosis for three-axis boring and milling machine
%B 2012
%9 stochastic resonance; fault diagnosis;boring and milling machine
%! AdSR based fault diagnosis for three-axis boring and milling machine
%K stochastic resonance; fault diagnosis;boring and milling machine
%X This paper introduced an adaptive stochastic resonance (AdSR) signal processing technique to extract fault feature of machining accuracy decay in boring and milling machine providing a vibration time-frequency distribution with adaptable precision. The AdSR uses correlation coefficient of the input signals and noise as a weight to construct the weighted kurtosis (WK) index. The influence of high frequency noise is alleviated and the index used in traditional SR is improved accordingly. The AdSR with WK can obtain optimal parameters adaptively. In addition, through the secondary utilization of noise, AdSR makes the signal output waveform smoother and the fluctuation period more obvious. It has been found that AdSR appears to be a better tool compared to fast Fourier transform for fault characterization extraction in boring and milling machine in experiment case. It was concluded that AdSR based signal processing technology successfully diagnosis the fault of machining accuracy decay in three-axis boring and milling machine.
%U https://www.sv-jme.eu/article/adsr-based-fault-diagnosis-for-three-axis-boring-and-milling-machine/
%0 Journal Article
%R 10.5545/sv-jme.2011.272
%& 527
%P 7
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 58
%N 9
%@ 0039-2480
%8 2018-06-28
%7 2018-06-28
Li, Bing.
"AdSR based fault diagnosis for three-axis boring and milling machine." Strojniški vestnik - Journal of Mechanical Engineering [Online], 58.9 (2012): 527-533. Web.  21 Feb. 2020
TY  - JOUR
AU  - Li, Bing 
PY  - 2012
TI  - AdSR based fault diagnosis for three-axis boring and milling machine
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2011.272
KW  - stochastic resonance; fault diagnosis;boring and milling machine
N2  - This paper introduced an adaptive stochastic resonance (AdSR) signal processing technique to extract fault feature of machining accuracy decay in boring and milling machine providing a vibration time-frequency distribution with adaptable precision. The AdSR uses correlation coefficient of the input signals and noise as a weight to construct the weighted kurtosis (WK) index. The influence of high frequency noise is alleviated and the index used in traditional SR is improved accordingly. The AdSR with WK can obtain optimal parameters adaptively. In addition, through the secondary utilization of noise, AdSR makes the signal output waveform smoother and the fluctuation period more obvious. It has been found that AdSR appears to be a better tool compared to fast Fourier transform for fault characterization extraction in boring and milling machine in experiment case. It was concluded that AdSR based signal processing technology successfully diagnosis the fault of machining accuracy decay in three-axis boring and milling machine.
UR  - https://www.sv-jme.eu/article/adsr-based-fault-diagnosis-for-three-axis-boring-and-milling-machine/
@article{{sv-jme}{sv-jme.2011.272},
	author = {Li, B.},
	title = {AdSR based fault diagnosis for three-axis boring and milling machine},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {58},
	number = {9},
	year = {2012},
	doi = {10.5545/sv-jme.2011.272},
	url = {https://www.sv-jme.eu/article/adsr-based-fault-diagnosis-for-three-axis-boring-and-milling-machine/}
}
TY  - JOUR
AU  - Li, Bing 
PY  - 2018/06/28
TI  - AdSR based fault diagnosis for three-axis boring and milling machine
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 58, No 9 (2012): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2011.272
KW  - stochastic resonance, fault diagnosis,boring and milling machine
N2  - This paper introduced an adaptive stochastic resonance (AdSR) signal processing technique to extract fault feature of machining accuracy decay in boring and milling machine providing a vibration time-frequency distribution with adaptable precision. The AdSR uses correlation coefficient of the input signals and noise as a weight to construct the weighted kurtosis (WK) index. The influence of high frequency noise is alleviated and the index used in traditional SR is improved accordingly. The AdSR with WK can obtain optimal parameters adaptively. In addition, through the secondary utilization of noise, AdSR makes the signal output waveform smoother and the fluctuation period more obvious. It has been found that AdSR appears to be a better tool compared to fast Fourier transform for fault characterization extraction in boring and milling machine in experiment case. It was concluded that AdSR based signal processing technology successfully diagnosis the fault of machining accuracy decay in three-axis boring and milling machine.
UR  - https://www.sv-jme.eu/article/adsr-based-fault-diagnosis-for-three-axis-boring-and-milling-machine/
Li, Bing"AdSR based fault diagnosis for three-axis boring and milling machine" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 58 Number 9 (28 June 2018)

Authors

Affiliations

  • Xi'an Jiaotong University 1

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 58(2012)9, 527-533

https://doi.org/10.5545/sv-jme.2011.272

This paper introduced an adaptive stochastic resonance (AdSR) signal processing technique to extract fault feature of machining accuracy decay in boring and milling machine providing a vibration time-frequency distribution with adaptable precision. The AdSR uses correlation coefficient of the input signals and noise as a weight to construct the weighted kurtosis (WK) index. The influence of high frequency noise is alleviated and the index used in traditional SR is improved accordingly. The AdSR with WK can obtain optimal parameters adaptively. In addition, through the secondary utilization of noise, AdSR makes the signal output waveform smoother and the fluctuation period more obvious. It has been found that AdSR appears to be a better tool compared to fast Fourier transform for fault characterization extraction in boring and milling machine in experiment case. It was concluded that AdSR based signal processing technology successfully diagnosis the fault of machining accuracy decay in three-axis boring and milling machine.

stochastic resonance; fault diagnosis;boring and milling machine