2001
DOI: 10.1006/jsvi.2001.3819
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Blind Source Separation: A Tool for Rotating Machine Monitoring by Vibrations Analysis?

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Cited by 94 publications
(43 citation statements)
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“…From the figure a, b,. It can be seen that in the normal state of the autocorrelation coefficient R.r (z) in the three cycles, the fault features are characterized by very good circular ring effect, and not in a bulge or burr phenomenon appears on the characteristic frequency, objectively illustrates the bearing is not failure, but keep a good state of normal operation [9] .…”
Section: Bearing Vibration Experimentsmentioning
confidence: 96%
“…From the figure a, b,. It can be seen that in the normal state of the autocorrelation coefficient R.r (z) in the three cycles, the fault features are characterized by very good circular ring effect, and not in a bulge or burr phenomenon appears on the characteristic frequency, objectively illustrates the bearing is not failure, but keep a good state of normal operation [9] .…”
Section: Bearing Vibration Experimentsmentioning
confidence: 96%
“…Many advanced techniques have been employed to detect and extract features from vibration signals [1][2][3][4][5][6]. Some conventional fault diagnosis techniques based on vibration signals extract the characteristic quantities from the time domain and frequency domain, statistical indexes, such as peak amplitude, root mean square amplitude, kurtosis and frequency components [7].…”
Section: Introductionmentioning
confidence: 99%
“…In such cases, a new contribution separation technique is necessary in order to identify high contributing sound sources and apply effective countermeasures. Accordingly, we have carried out basic studies to develop a new method utilizing independent component analysis (ICA) (Hyvarinen et al, 2001;Gelle et al, 2001;Araki et al, 2008;Yoshida and Ishihara, 2013;Yoshida and Hirano, 2014); the method does not require any input signals or transfer functions. In the ICA technique, sound source signals having independence from each other are estimated using their statistical characteristics (central limit theorem) and optimization theorem (Hyvarinen et al, 2001).…”
Section: Introductionmentioning
confidence: 99%