Identification of the number and locations of acoustic sources

Wang, J.H.; Chang, Chun-Tien

doi:10.1016/j.jsv.2004.06.006

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Hao and Chu (2009) present a scheme with morphological undecimated wavelet decomposition, where the noise produced from a rolling element bearing defect is analyzed for fault diagnostics. Wang and Chang (2005) present an application of the MUSIC algorithm applied to the identification of the number and localization of acoustic sources. Sarradj (2010) presents an application of the MUSIC algorithm applied to phased microphone array for correct quantitative estimation of acoustic source spectra.…”

Section: Introductionmentioning

confidence: 99%

Application of high-resolution spectral analysis for identifying faults in induction motors by means of sound

García-Pérez

Romero-Troncoso

Cabal-Yépez

et al. 2011

Journal of Vibration and Control

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Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified. Ever since the first motor was built, plant personnel have listened to the noises emanating from machines; with enough experience, a listener may make a fairly accurate estimate of the condition of a machine. Although there are several works that deal with vibration and current analysis for monitoring and detection of faults in induction motors, the analysis of sound signals has not been sufficiently explored as an alternative non-invasive monitoring technique. The contribution of this investigation is the development of a condition monitoring strategy than can make a reliable assessment of the presence of specific fault condition in an induction motor with a single fault present through the analysis of a sound signal. The proposed methodology is based on the multiple-signal classification algorithm for high-resolution spectral analysis. Results show that the proposed methodology of sound analysis could improve standard techniques for induction motor fault detection, enhancing detectability.

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Section: Introductionmentioning

confidence: 99%