Nothing Special   »   [go: up one dir, main page]

Su et al., 2005 - Google Patents

A neural network method for induction machine fault detection with vibration signal

Su et al., 2005

Document ID
1655478467934245145
Author
Su H
Chong K
Parlos A
Publication year
Publication venue
Computational Science and Its Applications–ICCSA 2005: International Conference, Singapore, May 9-12, 2005, Proceedings, Part II 5

External Links

Snippet

Early detection and diagnosis of induction machine incipient faults are desirable for online condition monitoring, product quality assurance, and improved operational efficiency. However, conventional methods have to work with explicit motor models and cannot be …
Continue reading at link.springer.com (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines
    • G01R31/343—Testing dynamo-electric machines in operation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric 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
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative 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
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric 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
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/02—Testing of electric apparatus, lines or components, for short-circuits, discontinuities, leakage of current, or incorrect line connection
    • G01R31/024—Arrangements for indicating continuity or short-circuits in electric apparatus or lines, leakage or ground faults
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric 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
    • G05B23/0243—Electric 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G01H1/003—Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines

Similar Documents

Publication Publication Date Title
Su et al. Induction machine condition monitoring using neural network modeling
Su et al. Vibration signal analysis for electrical fault detection of induction machine using neural networks
Bessous et al. Diagnosis of bearing defects in induction motors using discrete wavelet transform
Yang et al. Feature knowledge based fault detection of induction motors through the analysis of stator current data
Pandarakone et al. Deep neural network based bearing fault diagnosis of induction motor using fast Fourier transform analysis
Hong et al. Early fault diagnosis and classification of ball bearing using enhanced kurtogram and Gaussian mixture model
Ayhan et al. Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors
Kim et al. Sensorless fault diagnosis of induction motors
Amanuel et al. Comparative analysis of signal processing techniques for fault detection in three phase induction motor
Heydarzadeh et al. A wavelet-based fault diagnosis approach for permanent magnet synchronous motors
Bacha et al. Induction machine fault detection using stray flux EMF measurement and neural network-based decision
Hamdani et al. Neural network technique for induction motor rotor faults classification-dynamic eccentricity and broken bar faults
Nguyen et al. Analysis and comparison of multiple features for fault detection and prognostic in ball bearings
Corne et al. Comparing MCSA with vibration analysis in order to detect bearing faults—A case study
Samanta et al. Minimum distance-based detection of incipient induction motor faults using Rayleigh quotient spectrum of conditioned vibration signal
Tian et al. A review of fault diagnosis for traction induction motor
Sun et al. A novel rolling bearing vibration impulsive signals detection approach based on dictionary learning
Djagarov et al. Overview of diagnostic methods for rotating electrical machines
KR20110108950A (en) Failure diagnosing apparatus using neural network and feature point and method for failure diagnosing thereof
Pandarakone et al. Fault classification of outer-race bearing damage in low-voltage induction motor with aid of fourier analysis and SVM
Afshar et al. Generalized roughness bearing fault diagnosis using time series analysis and gradient boosted tree
Su et al. A neural network method for induction machine fault detection with vibration signal
Lim et al. Motor fault detection method for vibration signal using FFT residuals
Huo et al. A comparative study of WPD and EMD for shaft fault diagnosis
Heydarzadeh et al. A model-based signal processing method for fault diagnosis in PMSM machine