@article {Lee:2020:0736-2935:5635,
title = "Frequency-driven Convolutional Neural Network for Enhancing Noise-Robustness of Bearing Fault Detection",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2020",
volume = "261",
number = "1",
publication date ="2020-10-12T00:00:00",
pages = "5635-5645",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2020/00000261/00000001/art00071",
author = "Lee, Soo Young and Hwang, Yunseob and Lee, Seungchul",
abstract = "Bearing fault detection has been a critical issue, in particular under noisy environments considering that a robust fault detection model should be designed to perform well under harsh and diverse operational circumstances. Recent existing works using deep learning-based methods for
noise-robust modeling have contributed to the capability of coping with a variety of environments. However, there still exist challenging tasks to enhance the performance as the influence of the noise increases. Herein, a convolutional neural network (CNN)-based model is proposed to achieve
anti-noise bearing fault diagnostics by taking into account spatial and frequency-wise characteristics. The proposed method exploits Hilbert-Huang transform (HHT) in order to extract mode-wise instantaneous and spectral features, as well as the CNN-based modeling via frequency-driven grouping
strategies. By utilizing mode-wise frequency characteristics in feature extraction and feature learning of deep learning framework, it is shown that our proposed method yields promising performance improvement for noise-robust fault diagnosis.",
}