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Predicting Remaining Useful Life of Ball Bearing Using an Independent Recurrent Neural Network

Authors:

C. H. LiAuthors Info & Claims

MSIE '20: Proceedings of the 2020 2nd International Conference on Management Science and Industrial Engineering

Pages 237 - 241

https://doi.org/10.1145/3396743.3396765

Published: 29 May 2020 Publication History

Abstract

Planning maintenance of facilities is an important role for production line. From preventive maintenance to predictive maintenance, the main purpose is cost down by reducing the chance of the unexpected shot down. Thus, this study intends to apply independent recurrent neural network (IndRNN), which is a kind of deep learning technique, and apply it to predict remaining useful life for the ball bearings using vibration signals. The result of the proposed method is compared with original RNN. The experimental results indicate that IndRNN is able to perform better than the other method in terms of score.

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Cited By

Serradilla OZugasti ERamirez de Okariz JRodriguez JZurutuza U(2021)Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine DataApplied Sciences10.3390/app1116737611:16(7376)Online publication date: 11-Aug-2021
https://doi.org/10.3390/app11167376

Index Terms

Predicting Remaining Useful Life of Ball Bearing Using an Independent Recurrent Neural Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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MSIE '20: Proceedings of the 2020 2nd International Conference on Management Science and Industrial Engineering

April 2020

341 pages

ISBN:9781450377065

DOI:10.1145/3396743

Copyright © 2020 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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College of Technology Management, National Tsing Hua University, Taiwan

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Association for Computing Machinery

New York, NY, United States

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Published: 29 May 2020

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MSIE 2020

MSIE 2020: 2020 2nd International Conference on Management Science and Industrial Engineering

April 7 - 9, 2020

Osaka, Japan

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Cited By

Serradilla OZugasti ERamirez de Okariz JRodriguez JZurutuza U(2021)Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine DataApplied Sciences10.3390/app1116737611:16(7376)Online publication date: 11-Aug-2021
https://doi.org/10.3390/app11167376

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