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research-article

Remaining Useful Life Prediction of Bearing Based on Deep Perceptron Neural Networks

Authors:

Jiawei YinAuthors Info & Claims

BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things

Pages 175 - 179

https://doi.org/10.1145/3289430.3289438

Published: 24 October 2018 Publication History

Abstract

The life assessment and prediction research of the bearing is the most important content of the bearing long life and high reliable research. A novel remaining useful life prediction of bearing model that is deep learning based on deep perceptron neural networks (DPNN) is proposed in the present paper. Wavelet packet energy feature is extracted and then middle layers of the perceptron neural networks constitute a multilayer neural network. After training, remaining useful life (RUL) of bearing can be predicted by the DPNN model according to previous data points. To confirm the effectiveness of DPNN, Least Squares Support Vector Machine (LS-SVM) is employed to present a comprehensive comparison. The experimental results show that DPNN can predict effectively the RUL of bearing with high prediction accuracy and strong robustness.

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Cited By

Ferreira CGonçalves G(2022)Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning MethodsJournal of Manufacturing Systems10.1016/j.jmsy.2022.05.01063(550-562)Online publication date: Apr-2022
https://doi.org/10.1016/j.jmsy.2022.05.010
Serradilla OZugasti ERodriguez JZurutuza U(2022)Deep learning models for predictive maintenance: a survey, comparison, challenges and prospectsApplied Intelligence10.1007/s10489-021-03004-y52:10(10934-10964)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-021-03004-y
Wang XGuo JWang JLiu CDu C(2021)Prediction of Bearing Remaining Useful Life Based on LSTM NetworkMechanical Engineering and Materials10.1007/978-3-030-68303-0_7(81-90)Online publication date: 25-Mar-2021
https://doi.org/10.1007/978-3-030-68303-0_7

Index Terms

Remaining Useful Life Prediction of Bearing Based on Deep Perceptron Neural Networks
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks

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cover image ACM Other conferences

BDIOT '18: Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things

October 2018

217 pages

ISBN:9781450365192

DOI:10.1145/3289430

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Deakin University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2018

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BDIOT 2018

BDIOT 2018: 2018 2nd International Conference on Big Data and Internet of Things

October 24 - 26, 2018

Beijing, China

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Overall Acceptance Rate 75 of 136 submissions, 55%

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Cited By

Ferreira CGonçalves G(2022)Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning MethodsJournal of Manufacturing Systems10.1016/j.jmsy.2022.05.01063(550-562)Online publication date: Apr-2022
https://doi.org/10.1016/j.jmsy.2022.05.010
Serradilla OZugasti ERodriguez JZurutuza U(2022)Deep learning models for predictive maintenance: a survey, comparison, challenges and prospectsApplied Intelligence10.1007/s10489-021-03004-y52:10(10934-10964)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s10489-021-03004-y
Wang XGuo JWang JLiu CDu C(2021)Prediction of Bearing Remaining Useful Life Based on LSTM NetworkMechanical Engineering and Materials10.1007/978-3-030-68303-0_7(81-90)Online publication date: 25-Mar-2021
https://doi.org/10.1007/978-3-030-68303-0_7

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