Li et al., 2020 - Google Patents
A systematic review of deep transfer learning for machinery fault diagnosisLi et al., 2020
View PDF- Document ID
- 14957765841799821482
- Author
- Li C
- Zhang S
- Qin Y
- Estupinan E
- Publication year
- Publication venue
- Neurocomputing
External Links
Snippet
With the popularization of the intelligent manufacturing, much attention has been paid in such intelligent computing methods as deep learning ones for machinery fault diagnosis. Thanks to the development of deep learning models, the interference of the human …
- 238000003745 diagnosis 0 title abstract description 228
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | A systematic review of deep transfer learning for machinery fault diagnosis | |
An et al. | Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method | |
Li et al. | Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis | |
Li et al. | Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism | |
He et al. | Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions | |
Ren et al. | A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis | |
Zhao et al. | Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery | |
Zhao et al. | Deep multi-scale convolutional transfer learning network: A novel method for intelligent fault diagnosis of rolling bearings under variable working conditions and domains | |
Li et al. | Fault diagnosis of rotating machinery based on combination of deep belief network and one-dimensional convolutional neural network | |
Niu et al. | An optimized adaptive PReLU-DBN for rolling element bearing fault diagnosis | |
Zhu et al. | Acoustic signal-based fault detection of hydraulic piston pump using a particle swarm optimization enhancement CNN | |
Cheng et al. | Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions | |
Yang et al. | Interpretability of deep convolutional neural networks on rolling bearing fault diagnosis | |
Qin et al. | A new supervised multi-head self-attention autoencoder for health indicator construction and similarity-based machinery RUL prediction | |
Su et al. | Hierarchical diagnosis of bearing faults using branch convolutional neural network considering noise interference and variable working conditions | |
Zhou et al. | Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short‐Time Fourier Transform and Convolutional Neural Network | |
Yu et al. | Improved Butterfly Optimizer‐Configured Extreme Learning Machine for Fault Diagnosis | |
Barakat et al. | Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues | |
Xu et al. | A novel convolutional transfer feature discrimination network for unbalanced fault diagnosis under variable rotational speeds | |
Xiao et al. | Multiscale dilated convolutional subdomain adaptation network with attention for unsupervised fault diagnosis of rotating machinery cross operating conditions | |
Wang et al. | Multiscale noise reduction attention network for aeroengine bearing fault diagnosis | |
Li et al. | Maximum margin Riemannian manifold-based hyperdisk for fault diagnosis of roller bearing with multi-channel fusion covariance matrix | |
Saufi et al. | Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal | |
Kamat et al. | Bearing fault detection using comparative analysis of random forest, ANN, and autoencoder methods | |
Nezamivand Chegini et al. | A new intelligent fault diagnosis method for bearing in different speeds based on the FDAF-score algorithm, binary particle swarm optimization, and support vector machine |