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

Xue et al., 2024 - Google Patents

A novel framework for motor bearing fault diagnosis based on multi-transformation domain and multi-source data

Xue et al., 2024

Document ID
3959537380199262510
Author
Xue Y
Wen C
Wang Z
Liu W
Chen G
Publication year
Publication venue
Knowledge-Based Systems

External Links

Snippet

Through the application of deep learning and multi-sensor data, fault features can be automatically extracted and valuable information can be integrated to tackle intricate challenges in motor bearing fault diagnosis. Most existing fusion models focus primarily on …
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
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting 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
    • 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
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • 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
    • G06K9/00496Recognising patterns in signals and combinations thereof
    • G06K9/00536Classification; Matching
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • 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

Similar Documents

Publication Publication Date Title
Hu et al. Data-driven fault diagnosis method based on compressed sensing and improved multiscale network
Xie et al. End to end multi-task learning with attention for multi-objective fault diagnosis under small sample
Lei et al. Fault diagnosis of wind turbine based on Long Short-term memory networks
Liu et al. Subspace network with shared representation learning for intelligent fault diagnosis of machine under speed transient conditions with few samples
CN111914883B (en) Spindle bearing state evaluation method and device based on deep fusion network
Yu et al. Multi-label fault diagnosis of rolling bearing based on meta-learning
Xia et al. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks
Xue et al. A novel framework for motor bearing fault diagnosis based on multi-transformation domain and multi-source data
Su et al. A novel method based on deep transfer unsupervised learning network for bearing fault diagnosis under variable working condition of unequal quantity
Lu et al. Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition
CN110543860B (en) Mechanical fault diagnosis method and system based on TJM (machine learning model) transfer learning
CN108830127A (en) A kind of rotating machinery fault feature intelligent diagnostic method based on depth convolutional neural networks structure
CN114818774A (en) Intelligent gearbox fault diagnosis method based on multi-channel self-calibration convolutional neural network
Yang et al. A novel intelligent fault diagnosis method of rolling bearings with small samples
CN113284046B (en) Remote sensing image enhancement and restoration method and network based on no high-resolution reference image
Islam et al. Motor bearing fault diagnosis using deep convolutional neural networks with 2d analysis of vibration signal
CN115481666B (en) Gearbox small sample fault diagnosis method, system and equipment
CN113537152A (en) Flow field state fault detection method based on deep neural network
Zhao et al. Fault diagnosis based on space mapping and deformable convolution networks
Wen et al. Bearing fault diagnosis via fusing small samples and training multi-state siamese neural networks
CN117592332A (en) Digital twinning-based gearbox model high-fidelity method, system and storage medium
Zhang et al. CarNet: A dual correlation method for health perception of rotating machinery
Liu et al. A multisensory time-frequency features fusion method for rotating machinery fault diagnosis under nonstationary case
Zheng et al. A general fault diagnosis framework for rotating machinery and its flexible application example
Zhou et al. An Interpretable Parallel Spatial CNN-LSTM Architecture for Fault Diagnosis in Rotating Machinery