Multiple-domain manifold for feature extraction in machinery fault diagnosis
• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular val...
Ausführliche Beschreibung
Autor*in: |
Gan, Meng [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2015 |
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Schlagwörter: |
Multiple-domain manifold (MDM) |
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Umfang: |
16 |
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Übergeordnetes Werk: |
Enthalten in: High-performance and self-calibrating multi-gas sensor interface to trace multiple gas species with sub-ppm level - Kwon, Yeong Min ELSEVIER, 2022, journal of the International Measurement Confederation (IMEKO), Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:75 ; year:2015 ; pages:76-91 ; extent:16 |
Links: |
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DOI / URN: |
10.1016/j.measurement.2015.07.042 |
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ELV023463805 |
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• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. |
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• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. |
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• Phase space reconstruction is utilized to construct 2-D matrices representing signals in time and frequency domains. • Singular value decomposition is employed to calculate the singular values of multiple domains as preliminary features. • Manifold learning is introduced to revise the singular values to obtain the MDM features for fault diagnosis. • Practical engineering cases verified the advantages of MDM features in machinery fault diagnosis. |
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Multiple-domain manifold for feature extraction in machinery fault diagnosis |
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