Diagnosis of bearing defects using tunable Q-wavelet transform
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저자
Nitin Upadhyay
Pavan Kumar Kankar
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제어번호
106057295
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학술지명
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
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권호사항
Vol.
32
No.
2
[
2018
]
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발행처
대한기계학회
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자료유형
학술저널
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수록면
549-558
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언어
English
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출판년도
2018
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등재정보
KCI등재
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판매처
'
Diagnosis of bearing defects using tunable Q-wavelet transform' 의 참고문헌
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Wavelet transform with tunable Q-factor
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The fractal geometry of nature, First Ed.
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Neural networks - A comprehensive foundation, Second Ed.
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Multifault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition
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Modelling of a rotor-ball bearings system using Timoshenko beam and effects of rotating shaft on their dynamics
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Least squares support vector machine classifiers
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Least squares support vector machine
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Improved use of continuous attributes in C4.5
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Fractals and the analysis of waveforms
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Fault diagnosis of ball bearings using machine learning methods
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Fault classification of ball bearing by rotation forest technique
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Fault Diagnosis of Ball Bearings Using Continuous Wavelet Transform
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Efficient fault diagnosis of ball bearing using relieff and random forest classifier
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Decision treebased data characterization for meta-learning
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Classification of cardiac sound signals using constrained tunable-Q wavelet transform
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Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals
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Artificial neural network design for fault identification in a rotor-bearing system
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Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor
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Approach to an irregular time series on the basis of the fractal theory
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ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES
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A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system
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A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM
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A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box
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Diagnosis of bearing defects using tunable Q-wavelet transform'
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