[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Ruan et al., 2022 - Google Patents

An enhanced non-local weakly supervised fault diagnosis method for rotating machinery

Ruan et al., 2022

Document ID
8015052475207291887
Author
Ruan H
Wang Y
Li X
Qin Y
Tang B
Publication year
Publication venue
Measurement

External Links

Snippet

Despite the considerable success achieved by deep learning-based fault diagnosis methods, a powerful deep learning model must require a substantial set of training data to obtain a strong generalization ability in practical application. Focusing on the fault diagnosis …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00Methods or arrangements for processing data by operating upon the order or content of the data handled
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis

Similar Documents

Publication Publication Date Title
Xu et al. CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery
Yu et al. Multi-label fault diagnosis of rolling bearing based on meta-learning
Wang et al. Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions
Zhang et al. Supervised contrastive learning-based domain adaptation network for intelligent unsupervised fault diagnosis of rolling bearing
Sinitsin et al. Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model
Han et al. Deep transfer learning with limited data for machinery fault diagnosis
Feng et al. Similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification
Sun et al. Sparse deep stacking network for fault diagnosis of motor
Ruan et al. An enhanced non-local weakly supervised fault diagnosis method for rotating machinery
Zhang et al. Ensemble deep contractive auto-encoders for intelligent fault diagnosis of machines under noisy environment
Jiang et al. Joint label consistent dictionary learning and adaptive label prediction for semisupervised machine fault classification
Jiang et al. Fault diagnosis for rolling bearing using a hybrid hierarchical method based on scale-variable dispersion entropy and parametric t-SNE algorithm
Liu et al. Data fusion generative adversarial network for multi-class imbalanced fault diagnosis of rotating machinery
Bai et al. Research on feature selection for rotating machinery based on Supervision Kernel Entropy Component Analysis with Whale Optimization Algorithm
Chen et al. Fault diagnosis for limited annotation signals and strong noise based on interpretable attention mechanism
CN109508740B (en) Object hardness identification method based on Gaussian mixed noise production confrontation network
Xu et al. A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems
Tang et al. A novel transfer learning network with adaptive input length selection and lightweight structure for bearing fault diagnosis
Dong et al. Intelligent fault diagnosis of rolling bearings based on refined composite multi-scale dispersion q-complexity and adaptive whale algorithm-extreme learning machine
Wang et al. Multiple local domains transfer network for equipment fault intelligent identification
Zhang et al. A bearing fault diagnosis method based on multiscale dispersion entropy and GG clustering
Zhang et al. Semi-supervised fault diagnosis of gearbox based on feature pre-extraction mechanism and improved generative adversarial networks under limited labeled samples and noise environment
Lei et al. A new transferable bearing fault diagnosis method with adaptive manifold probability distribution under different working conditions
Yang et al. Transfer graph-driven rotating machinery diagnosis considering cross-domain relationship construction
Wen et al. Bearing fault diagnosis via fusing small samples and training multi-state siamese neural networks