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Authordc.contributor.authorZhang, Bin 
Authordc.contributor.authorKhawaja, Taimoor es_CL
Authordc.contributor.authorPatrick, Romano es_CL
Authordc.contributor.authorVachtsevanos, George es_CL
Authordc.contributor.authorOrchard Concha, Marcos es_CL
Authordc.contributor.authorSaxena, Abhinav es_CL
Admission datedc.date.accessioned2010-07-05T13:07:36Z
Available datedc.date.available2010-07-05T13:07:36Z
Publication datedc.date.issued2010
Cita de ítemdc.identifier.citationTransactions of the Institute of Measurement and Control 32, 1 (2010) pp. 3–30en_US
Identifierdc.identifier.other10.1177/0142331209357844
Identifierdc.identifier.urihttps://repositorio.uchile.cl/handle/2250/125396
Abstractdc.description.abstractWith increased system complexity, condition-based maintenance (CBM) becomes a promising solution for system safety by detecting faults and scheduling maintenance procedures before faults become severe failures resulting in catastrophic events. For CBM of many mechanical systems, fault diagnosis and failure prognosis based on vibration signal analysis are essential techniques. Noise originating from various sources, however, often corrupts vibration signals and degrades the performance of diagnostic and prognostic routines, and consequently, the performance of CBM. In this paper, a new de-noising structure is proposed and applied to vibration signals collected from a testbed of the main gearbox of a helicopter subjected to a seeded fault. The proposed structure integrates a blind deconvolution algorithm, feature extraction, failure prognosis and vibration modelling into a synergistic system, in which the blind deconvolution algorithm attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes associated with quality of the extracted features and failure prognosis are addressed, before and after de-noising, for validation purposes.en_US
Patrocinadordc.description.sponsorshipThe research reported in this paper was partially supported by DARPA and Northrop Grumman under the DARPA Prognosis program Contract No. HR0011-04-C-003. We gratefully acknowledge the support and assistance received from our sponsors.en_US
Lenguagedc.language.isoenen_US
Keywordsdc.subjectblind deconvolutionen_US
Títulodc.titleA novel blind deconvolution de-noising scheme in failure prognosisen_US
Document typedc.typeArtículo de revista


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