Intelligent Fault Diagnosis of Rolling Element Bearings Based on HHT and CNN

Automatic and accurate identification of rolling bearings fault categories and fault severities is still a major challenge in fault diagnosis. In this paper, a deep learning based approach is presented to translate traditional diagnostic methods based on one-dimensional time-series analysis into gra...

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Bibliographic Details
Published in2018 Prognostics and System Health Management Conference (PHM-Chongqing) pp. 292 - 296
Main Authors Yuan, Zhuang, Zhang, Laibin, Duan, Lixiang, Li, Tao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
CNN
Convolution
Deep learning
Fault diagnosis
Feature extraction
HHT
Manuals
Time-frequency analysis
Transforms
Vibrations
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Summary:Automatic and accurate identification of rolling bearings fault categories and fault severities is still a major challenge in fault diagnosis. In this paper, a deep learning based approach is presented to translate traditional diagnostic methods based on one-dimensional time-series analysis into graphical images for fault type and severity identification, with rolling bearing as a representative example. Specifically, time sequences of vibration signals are first converted by Hilbert-Huang transform (HHT) to time-frequency images. Next, a convolutional neural network (CNN) learns fault-sensitive features in the time-frequency domain from these images and performs fault classification. Experiments on bearing data demonstrates effectiveness and efficiency of the developed approach with a classification accuracy 95%.
ISSN:2166-5656
DOI:10.1109/PHM-Chongqing.2018.00056