HHT Sifting and Filtering

Meeson, Reginald N.

doi:10.1142/9789814508247_0006

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Hilbert-huang Transform1

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2017

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“…This results in a negative local derivative. For further information regarding this property of HHT, the reader is directed to read Meeson [32].…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

291

198

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Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Timefrequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.

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“…This results in a negative local derivative. For further information regarding this property of HHT, the reader is directed to read Meeson [32].…”

Section: Hilbert-huang Transformmentioning

confidence: 99%