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

Shen et al., 2014 - Google Patents

Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines

Shen et al., 2014

View PDF
Document ID
12163935246294714656
Author
Shen C
Wang D
Liu Y
Kong F
Tse P
Publication year
Publication venue
Smart Structures and Systems

External Links

Snippet

The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural …
Continue reading at www.academia.edu (PDF) (other versions)

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
    • G06K9/6268—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
    • 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
    • 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
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Shen et al. Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines
Guo et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings
Rohani Bastami et al. Estimation of remaining useful life of rolling element bearings using wavelet packet decomposition and artificial neural network
Lang et al. Artificial intelligence-based technique for fault detection and diagnosis of EV motors: A review
Zhang et al. A fault diagnosis method based on improved convolutional neural network for bearings under variable working conditions
Cerrada et al. A review on data-driven fault severity assessment in rolling bearings
Rai et al. An integrated approach to bearing prognostics based on EEMD-multi feature extraction, Gaussian mixture models and Jensen-Rényi divergence
Li et al. Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method
Chen et al. Railway turnout system RUL prediction based on feature fusion and genetic programming
Ma et al. Locally linear embedding on Grassmann manifold for performance degradation assessment of bearings
Dong et al. Bearing degradation process prediction based on the support vector machine and Markov model
Zhang et al. Bearing performance degradation assessment based on time-frequency code features and SOM network
Ma et al. Subspace-based MVE for performance degradation assessment of aero-engine bearings with multimodal features
Martínez-Rego et al. Fault detection via recurrence time statistics and one-class classification
Kumar et al. The Importance of Feature Processing in Deep‐Learning‐Based Condition Monitoring of Motors
Chen et al. Explainable deep ensemble model for bearing fault diagnosis under variable conditions
Peco Chacón et al. State of the art of artificial intelligence applied for false alarms in wind turbines
Wu et al. Design a degradation condition monitoring system scheme for rolling bearing using EMD and PCA
Vives Vibration analysis for fault detection in wind turbines using machine learning techniques
Wang et al. An improved fusion prognostics method for remaining useful life prediction of bearings
Cui et al. Application of rotating machinery fault diagnosis based on deep learning
Magadán et al. Robust prediction of remaining useful lifetime of bearings using deep learning
BENKEDJOUH et al. Deep Learning for Fault Diagnosis based on short-time Fourier transform
Pang et al. Discrete Cosine Transformation and Temporal Adjacent Convolutional Neural Network‐Based Remaining Useful Life Estimation of Bearings
Yi et al. On a prediction method for remaining useful life of rolling Bearings via VMD-based dispersion entropy and GAN