Liu et al., 2023 - Google Patents
A review on deep learning in planetary gearbox health state recognition: Methods, applications, and dataset publicationLiu et al., 2023
View PDF- Document ID
- 767175116892133310
- Author
- Liu D
- Cui L
- Cheng W
- Publication year
- Publication venue
- Measurement Science and Technology
External Links
Snippet
Planetary gearboxes have various merits in mechanical transmission, but their complex structure and intricate operation modes bring large challenges in terms of fault diagnosis. Deep learning has attracted increasing attention in intelligent fault diagnosis and has been …
Classifications
-
- 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
- G06K9/6284—Single class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- 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
- 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
- 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
-
- 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
-
- 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
-
- 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
- G06K9/36—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
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Liu et al. | A review on deep learning in planetary gearbox health state recognition: Methods, applications, and dataset publication | |
Li et al. | Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis | |
Li et al. | Diagnosing rotating machines with weakly supervised data using deep transfer learning | |
Qian et al. | A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis | |
Han et al. | A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions | |
Zhang et al. | Federated learning for machinery fault diagnosis with dynamic validation and self-supervision | |
Lang et al. | Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review | |
Zhang et al. | Machinery fault diagnosis with imbalanced data using deep generative adversarial networks | |
Yan et al. | Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions | |
Kong et al. | A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings | |
Li et al. | Variational attention-based interpretable transformer network for rotary machine fault diagnosis | |
Wu et al. | An integrated ensemble learning model for imbalanced fault diagnostics and prognostics | |
Sohaib et al. | Fault diagnosis of rotary machine bearings under inconsistent working conditions | |
Yang et al. | Deep multiple auto-encoder with attention mechanism network: A dynamic domain adaptation method for rotary machine fault diagnosis under different working conditions | |
Huang et al. | Compound fault diagnosis for rotating machinery: State-of-the-art, challenges, and opportunities | |
Han et al. | Cross‐machine intelligent fault diagnosis of gearbox based on deep learning and parameter transfer | |
Yang et al. | Twin broad learning system for fault diagnosis of rotating machinery | |
Liu et al. | Intelligent fault diagnosis under small sample size conditions via Bidirectional InfoMax GAN with unsupervised representation learning | |
Liu et al. | Counterfactual-augmented few-shot contrastive learning for machinery intelligent fault diagnosis with limited samples | |
Kumar et al. | The Importance of Feature Processing in Deep‐Learning‐Based Condition Monitoring of Motors | |
Xin et al. | Intelligent fault diagnosis method for rotating machinery based on vibration signal analysis and hybrid multi‐object deep CNN | |
Su et al. | Application of deep learning to fault diagnosis of rotating machineries | |
Sun et al. | Intelligent fault diagnosis of rotating machinery under varying working conditions with global–local neighborhood and sparse graphs embedding deep regularized autoencoder | |
Cao et al. | Fault diagnosis of rolling bearing based on multiscale one-dimensional hybrid binary pattern | |
Wang et al. | A multi-branch convolutional transfer learning diagnostic method for bearings under diverse working conditions and devices |