17 resultados para 1888-1978 -- Criticism and interpretation
Filtro por publicador
- ABACUS. Repositorio de Producción Científica - Universidad Europea (1)
- Aberdeen University (5)
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- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (3)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (1)
- Aquatic Commons (23)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archive of European Integration (39)
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- Aston University Research Archive (21)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (7)
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- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (5)
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- Blue Tiger Commons - Lincoln University - USA (1)
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- Cambridge University Engineering Department Publications Database (17)
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- Chinese Academy of Sciences Institutional Repositories Grid Portal (2)
- Cochin University of Science & Technology (CUSAT), India (2)
- Coffee Science - Universidade Federal de Lavras (1)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (5)
- CORA - Cork Open Research Archive - University College Cork - Ireland (12)
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- Duke University (2)
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- Harvard University (3)
- Helda - Digital Repository of University of Helsinki (26)
- Indian Institute of Science - Bangalore - Índia (26)
- Instituto Politécnico do Porto, Portugal (3)
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- University of Michigan (62)
- University of Queensland eSpace - Australia (23)
- University of Washington (4)
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- Worcester Research and Publications - Worcester Research and Publications - UK (2)
Resumo:
Condition-based maintenance is concerned with the collection and interpretation of data to support maintenance decisions. The non-intrusive nature of vibration data enables the monitoring of enclosed systems such as gearboxes. It remains a significant challenge to analyze vibration data that are generated under fluctuating operating conditions. This is especially true for situations where relatively little prior knowledge regarding the specific gearbox is available. It is therefore investigated how an adaptive time series model, which is based on Bayesian model selection, may be used to remove the non-fault related components in the structural response of a gear assembly to obtain a residual signal which is robust to fluctuating operating conditions. A statistical framework is subsequently proposed which may be used to interpret the structure of the residual signal in order to facilitate an intuitive understanding of the condition of the gear system. The proposed methodology is investigated on both simulated and experimental data from a single stage gearbox. © 2011 Elsevier Ltd. All rights reserved.