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

Zhou et al., 2018 - Google Patents

A new method to classify railway vehicle axle fatigue crack AE signal

Zhou et al., 2018

Document ID
3776913832074004716
Author
Zhou Y
Lin L
Wang D
He M
He D
Publication year
Publication venue
Applied Acoustics

External Links

Snippet

This paper presents a new method for classifying railway vehicle axle fatigue crack acoustic emission (AE) signal. The method is developed by integrating self-adaptive empirical mode decomposition (EMD) with Elman neural network (ENN). The method first uses EMD to …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • 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
    • 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
    • G01N29/4445—Classification of defects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/26—Scanned objects
    • G01N2291/269—Various geometry objects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/02—Indexing codes associated with the analysed material
    • G01N2291/028—Material parameters
    • 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
    • G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
    • 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
    • G01N29/22—Details, e.g. general constructional or apparatus details
    • G01N29/24—Probes
    • G01N29/2493—Wheel shaped probes

Similar Documents

Publication Publication Date Title
Zhou et al. A new method to classify railway vehicle axle fatigue crack AE signal
Li et al. Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter
Li et al. Fault detection method for railway wheel flat using an adaptive multiscale morphological filter
Cui et al. Quantitative trend fault diagnosis of a rolling bearing based on Sparsogram and Lempel-Ziv
CN105241660B (en) High-speed rail large bridge performance test methods based on health monitoring data
CN112858473B (en) Turnout switch blade damage state monitoring method based on feature fusion
CN101900708A (en) Vibration and audio signal-based high-speed train track defect detecting method
CN106796579A (en) For the method and system of the defect in automatic detection rotary shaft
O'brien et al. A pattern recognition system based on acoustic signals for fault detection on composite materials
CN106841403A (en) A kind of acoustics glass defect detection method based on neutral net
CN105510438A (en) Operating vehicle-based rail nucleus flaw detection system and method
Madain et al. Fault diagnosis in vehicle engines using sound recognition techniques
Yu et al. Novel hybrid method based on advanced signal processing and soft computing techniques for condition assessment of timber utility poles
Li et al. Mobile robot motor bearing fault detection and classification on discrete wavelet transform and LSTM network
CN106546918A (en) A kind of method for diagnosing faults of Hydropower Unit
Zhang et al. An investigation on acoustic emission detection of rail crack in actual application by chaos theory with improved feature detection method
Patel et al. Fault diagnostics of rolling bearing based on improve time and frequency domain features using artificial neural networks
Yu et al. Automated health condition diagnosis of in situ wood utility poles using an intelligent non-destructive evaluation (NDE) framework
Yang et al. Crack detection in carbide anvil using acoustic signal and deep learning with particle swarm optimisation
CN111855793A (en) Seamless rail internal temperature stress early diagnosis method based on surface magnetic memory signal
CN107345858A (en) Method for rapidly extracting train bearing rail edge signal impact components
CN112986388B (en) Turnout switch blade defect detection method and system based on broadband excitation
Hopkins et al. Broken rail prediction and detection using wavelets and artificial neural networks
CN102890119A (en) Characterization and quantitative evaluation system for fatigue damage state of in-service AZ31 magnesium alloy structural member based on WPT (Wavelet Packet Transform) strategy and DF (Dual-Frequency) strategy
Unal et al. Fault diagnosis of rolling bearing based on feature extraction and neural network algorithm