2012
DOI: 10.1007/s00542-012-1501-5
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Probing and diagnosis of slider–disk interactions in nanometer clearance regime using artificial neural network

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Cited by 5 publications
(3 citation statements)
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“…The prognostics and health management and health monitoring technology have been fully developed by the professional community of electronics, aerospace and civil engineering [48,49]. In [50][51][52][53][54][55][56], a framework of diagnosis and prognosis has been implemented and developed for contact/frictioninduced vibrations of near contact slider in hard disk drive system. The similar technology can be applied to tackle with frictioninduced friction and noise problems in automotive systems.…”
Section: Diagnosis and Prognosismentioning
confidence: 99%
“…The prognostics and health management and health monitoring technology have been fully developed by the professional community of electronics, aerospace and civil engineering [48,49]. In [50][51][52][53][54][55][56], a framework of diagnosis and prognosis has been implemented and developed for contact/frictioninduced vibrations of near contact slider in hard disk drive system. The similar technology can be applied to tackle with frictioninduced friction and noise problems in automotive systems.…”
Section: Diagnosis and Prognosismentioning
confidence: 99%
“…The recent implementation of the contemporary nonlinear identification methods offers some promising approaches to identify certain core parameters of slider dynamics in sub-5 nanometer clearance regime by using experimental data [121][122][123][124][125]. In [121], the nonstationary and nonlinear response of sub-10-nm clearance air-bearing slider induced by a bump contact is recorded using laser Doppler vibrometer (LDV) and studied using FFT, power spectrum density, spectrogram, and Hilbert instantaneous spectrum analysis.…”
mentioning
confidence: 99%
“…The approach of the combination of power spectrum density of decomposed signal and Hilbert spectrum is more effective than conventional time-frequency analysis and Wavelet transform for identifying spectrum signature of slider contact dynamics, which exhibit nonstationary and nonlinear dynamics properties. The work in [125] proposed to use artificial neural network to map the varied spectrum patterns of the slider response in nanometer clearance regime from simulations and existing testing database, which offers an efficient tool for experimentalists to design laboratory test and conduct parameter identifications.…”
mentioning
confidence: 99%