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

Huang et al., 2021 - Google Patents

Research on fan vibration fault diagnosis based on image recognition

Huang et al., 2021

View HTML
Document ID
5997280812303800730
Author
Huang G
Qiao L
Khanna S
Pavlovich P
Tiwari S
Publication year
Publication venue
Journal of Vibroengineering

External Links

Snippet

The conventional methods for vibration fault detection and diagnosis relies on feature extraction from the waveforms of the vibration signals. This article exploits the scope of image recognition application for the detection and diagnosis of fan vibration faults. In this …
Continue reading at www.extrica.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • 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
    • 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
    • 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
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions

Similar Documents

Publication Publication Date Title
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
Udmale et al. Application of spectral kurtosis and improved extreme learning machine for bearing fault classification
Principi et al. Unsupervised electric motor fault detection by using deep autoencoders
Cai et al. Data-driven early fault diagnostic methodology of permanent magnet synchronous motor
Han et al. A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions
Grezmak et al. Interpretable convolutional neural network through layer-wise relevance propagation for machine fault diagnosis
Li et al. A deep learning driven method for fault classification and degradation assessment in mechanical equipment
Huang et al. Memory residual regression autoencoder for bearing fault detection
Dong et al. Bearing degradation process prediction based on the PCA and optimized LS-SVM model
Lu et al. Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition
CN103597417B (en) state monitoring method and device
Huang et al. Research on fan vibration fault diagnosis based on image recognition
Sikder et al. Fault diagnosis of motor bearing using ensemble learning algorithm with FFT-based preprocessing
Barakat et al. Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues
CN110907732A (en) Phase modulator fault diagnosis method based on PCA-RBF neural network
Kumar et al. The Importance of Feature Processing in Deep‐Learning‐Based Condition Monitoring of Motors
CN107644231A (en) A kind of generator amature method for diagnosing faults and device
Lv et al. Deep transfer network with multi-kernel dynamic distribution adaptation for cross-machine fault diagnosis
Elmasry et al. Detection of faults in electrical power grids using an enhanced anomaly-based method
Jamil et al. Influence of one-way ANOVA and Kruskal–Wallis based feature ranking on the performance of ML classifiers for bearing fault diagnosis
Jia et al. Review on engine vibration fault analysis based on data mining
Haroun et al. Feature selection for enhancement of bearing fault detection and diagnosis based on self-organizing map
Yao et al. Anomaly detection of steam turbine with hierarchical pre‐warning strategy
Ding et al. Machine tool fault classification diagnosis based on audio parameters