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

Bin et al., 2012 - Google Patents

Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network

Bin et al., 2012

Document ID
17090083307006286821
Author
Bin G
Gao J
Li X
Dhillon B
Publication year
Publication venue
Mechanical Systems and Signal Processing

External Links

Snippet

After analyzing the shortcomings of current feature extraction and fault diagnosis technologies, a new approach based on wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) are combined to extract fault feature frequency and …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Testing of gearing or of transmission mechanisms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/46—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by spectral analysis, e.g. Fourier analysis or wavelet analysis
    • 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
Bin et al. Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network
Manhertz et al. STFT spectrogram based hybrid evaluation method for rotating machine transient vibration analysis
Li et al. Application of bandwidth EMD and adaptive multiscale morphology analysis for incipient fault diagnosis of rolling bearings
Zheng et al. Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD
Yongbo et al. Review of local mean decomposition and its application in fault diagnosis of rotating machinery
Jiang et al. Sparse dictionary design based on edited cepstrum and its application in rolling bearing fault diagnosis
He et al. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis
Ma et al. Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform
Zheng et al. Spectral envelope-based adaptive empirical Fourier decomposition method and its application to rolling bearing fault diagnosis
Hu et al. Vibration signal denoising method based on CEEMDAN and its application in brake disc unbalance detection
Li et al. Early fault diagnosis of rotating machinery by combining differential rational spline-based LMD and K–L divergence
Xu et al. Generalized S-synchroextracting transform for fault diagnosis in rolling bearing
Li et al. Rotating machinery fault diagnosis based on typical resonance demodulation methods: a review
Cui et al. Fault diagnosis of offshore wind turbines based on component separable synchroextracting transform
Shuuji et al. Low-speed bearing fault diagnosis based on improved statistical filtering and convolutional neural network
He et al. A data-driven group-sparse feature extraction method for fault detection of wind turbine transmission system
Shi et al. The VMD-scale space based hoyergram and its application in rolling bearing fault diagnosis
Xu et al. An adaptive spectrum segmentation method to optimize empirical wavelet transform for rolling bearings fault diagnosis
Yuan et al. Dual-core denoised synchrosqueezing wavelet transform for gear fault detection
Zheng et al. An adaptive group sparse feature decomposition method in frequency domain for rolling bearing fault diagnosis
Zhao et al. Adaptive scaling demodulation transform: Algorithm and applications
Wei et al. Fault diagnosis of bearings in multiple working conditions based on adaptive time-varying parameters short-time Fourier synchronous squeeze transform
Li et al. Multi-fault diagnosis of rotating machinery via iterative multivariate variational mode decomposition
Dong et al. Incipient bearing fault feature extraction based on minimum entropy deconvolution and K-singular value decomposition
Li et al. Oscillatory time–frequency concentration for adaptive bearing fault diagnosis under nonstationary time-varying speed