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

Tao et al., 2015 - Google Patents

Fault diagnosis of rolling bearing using deep belief networks

Tao et al., 2015

View PDF
Document ID
2914251646485475265
Author
Tao J
Liu Y
Yang D
Tang F
Liu C
Publication year
Publication venue
2015 International Symposium on Material, Energy and Environment Engineering

External Links

Snippet

This paper presents an approach to implement vibration signals for fault diagnosis of the rolling bearing. Due to the noise and transient impacts, it is difficulty to accurately diagnosis the faults with traditional methods. So a new type of learning architecture for deep …
Continue reading at www.atlantis-press.com (PDF) (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
    • G06K9/62Methods 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
    • 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
    • G06N3/08Learning methods
    • G06N3/082Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • 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
    • G06K9/6284Single class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • 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
    • G06K9/68Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
    • G06K9/6807Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries
    • G06K9/6842Dividing the references in groups prior to recognition, the recognition taking place in steps; Selecting relevant dictionaries according to the linguistic properties, e.g. English, German
    • 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
    • G06N3/04Architectures, e.g. interconnection topology
    • 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
    • G06K9/4604Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections

Similar Documents

Publication Publication Date Title
Hu et al. Data-driven fault diagnosis method based on compressed sensing and improved multiscale network
Dai et al. Machinery health monitoring based on unsupervised feature learning via generative adversarial networks
Tao et al. Fault diagnosis of rolling bearing using deep belief networks
Chen et al. Deep neural networks-based rolling bearing fault diagnosis
Li et al. A deep transfer nonnegativity-constraint sparse autoencoder for rolling bearing fault diagnosis with few labeled data
CN110617966A (en) Bearing fault diagnosis method based on semi-supervised generation countermeasure network
Pan et al. A deep learning network via shunt-wound restricted Boltzmann machines using raw data for fault detection
CN110398369A (en) A kind of Fault Diagnosis of Roller Bearings merged based on 1-DCNN and LSTM
Lu et al. A novel feature extraction method using deep neural network for rolling bearing fault diagnosis
Zhang et al. A multi-module generative adversarial network augmented with adaptive decoupling strategy for intelligent fault diagnosis of machines with small sample
CN110375987A (en) One kind being based on depth forest machines Bearing Fault Detection Method
CN113865868A (en) Rolling bearing fault diagnosis method based on time-frequency domain expression
CN113865872B (en) Bearing fault diagnosis method based on wavelet packet reconstruction imaging and CNN
Zhang et al. An intelligent fault diagnosis method of rolling bearing under variable working loads using 1-D stacked dilated convolutional neural network
Zhou et al. Fault detection of rolling bearing based on FFT and classification
Guo et al. A method of rolling bearing fault diagnose based on double sparse dictionary and deep belief network
Grezmak et al. Multi-stream convolutional neural network-based fault diagnosis for variable frequency drives in sustainable manufacturing systems
CN115290326A (en) Rolling bearing fault intelligent diagnosis method
Deng et al. Rolling bearing fault diagnosis based on Deep Boltzmann machines
CN114091504A (en) Rotary machine small sample fault diagnosis method based on generation countermeasure network
Neupane et al. Deep learning-based bearing fault detection using 2-D illustration of time sequence
CN115326398B (en) Bearing fault diagnosis method based on fuzzy width learning model
Ahmed et al. Effects of deep neural network parameters on classification of bearing faults
BENKEDJOUH et al. Deep Learning for Fault Diagnosis based on short-time Fourier transform
Yu et al. Rolling bearing fault feature extraction and diagnosis method based on MODWPT and DBN