Qian et al., 2021 - Google Patents
A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosisQian et al., 2021
- Document ID
- 3913389930985980220
- Author
- Qian Q
- Qin Y
- Wang Y
- Liu F
- Publication year
- Publication venue
- Measurement
External Links
Snippet
Deep learning has gained a great achievement in the intelligent fault diagnosis of rotating machineries. However, the labeled data is scarce in actual engineering and the marginal distribution of data is discrepant under different conditions. Transfer learning provides a …
- 238000003745 diagnosis 0 title abstract description 77
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- G06K9/6279—Classification techniques relating to the number of classes
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- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N5/02—Knowledge representation
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- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
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- G06F17/141—Discrete Fourier transforms
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