Li et al., 2019 - Google Patents
A deep transfer nonnegativity-constraint sparse autoencoder for rolling bearing fault diagnosis with few labeled dataLi et al., 2019
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- 4925371119023437192
- Author
- Li X
- Jiang H
- Zhao K
- Wang R
- Publication year
- Publication venue
- IEEE access
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Rolling bearing fault diagnosis can greatly improve the safety of rotating machinery. In some cases, plenty of labeled data are unavailable, which may lead to low diagnosis accuracy. To deal with this problem, a deep transfer nonnegativity-constraint sparse autoencoder …
- 238000003745 diagnosis 0 title abstract description 59
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- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
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- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
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- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
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