IEEE Systems, Man and Cybernetics Magazine - January 2023 - 18
Bearing
Degradation
Prediction by
WPD and DPNN
Introducing a Novel
Deep Learning Method
by Sheng Hong , Xiaochuan Duan,
Yao Peng, Hao Liu
, and Enrico Zio
I
n this article, a novel method of deep learning based
on wavelet transform and deep perceptron neural
networks (DPNNs) is proposed to predict the
remaining useful life (RUL) of bearings. The proposed
approach first extracts from the recorded signals
the energy features by the wavelet packet transform.
After training on these data, the DPNN model can be used
to predict the RUL of a bearing. To verify the model, the
proposed DPNN based on wavelet packet transform is
compared with a least-squares support vector machine
(LS-SVM) and long short-term memory (LSTM). The
experimental results illustrate that DPNN can effectively
predict the RUL of the bearing and is superior to the LSSVM
and LSTM in terms of prediction performance.
Introduction
Bearings are very important in industrial and mechanical
equipment for agriculture, transportation, and often engineering
applications [1]. It is estimated that 30% of the
causes of mechanical failures are due to bearing damage.
These defective bearings are likely to cause equipment
failure [2]. Therefore, the timely degradation prediction
Digital Object Identifier 10.1109/MSMC.2022.3218424
Date of current version: 10 January 2023
18 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE January 2023
2333-942X/23©2023IEEE
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IEEE Systems, Man and Cybernetics Magazine - January 2023
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