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article

Predicting remaining useful life of rotating machinery based artificial neural network

Authors:

Abd Kadir Mahamad,

Takashi HiyamaAuthors Info & Claims

Computers & Mathematics with Applications, Volume 60, Issue 4

Pages 1078 - 1087

https://doi.org/10.1016/j.camwa.2010.03.065

Published: 01 August 2010 Publication History

Abstract

Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure.

References

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Cited By

Shaheen BKocsis ÁNémeth I(2023)Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networksEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105749119:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105749
El-Brawany MAdel Ibrahim DElminir HElattar HRamadan E(2023)Artificial intelligence-based data-driven prognostics in industryComputers and Industrial Engineering10.1016/j.cie.2023.109605184:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.cie.2023.109605
Remadna ITerrissa SSayah MAyad SZerhouni N(2022)Boosting RUL Prediction Using a Hybrid Deep CNN-BLSTM ArchitectureAutomatic Control and Computer Sciences10.3103/S014641162204006X56:4(300-310)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.3103/S014641162204006X
Show More Cited By

Predicting remaining useful life of rotating machinery based artificial neural network
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
  2. Modeling and simulation

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Published In

cover image Computers & Mathematics with Applications

Computers & Mathematics with Applications Volume 60, Issue 4

August, 2010

214 pages

ISSN:0898-1221

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2010.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 August 2010

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Cited By

Shaheen BKocsis ÁNémeth I(2023)Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networksEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105749119:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105749
El-Brawany MAdel Ibrahim DElminir HElattar HRamadan E(2023)Artificial intelligence-based data-driven prognostics in industryComputers and Industrial Engineering10.1016/j.cie.2023.109605184:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.cie.2023.109605
Remadna ITerrissa SSayah MAyad SZerhouni N(2022)Boosting RUL Prediction Using a Hybrid Deep CNN-BLSTM ArchitectureAutomatic Control and Computer Sciences10.3103/S014641162204006X56:4(300-310)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.3103/S014641162204006X
Zhu QXiong QYang ZYu Y(2022)A novel feature-fusion-based end-to-end approach for remaining useful life predictionJournal of Intelligent Manufacturing10.1007/s10845-022-02015-x34:8(3495-3505)Online publication date: 10-Sep-2022
https://dl.acm.org/doi/10.1007/s10845-022-02015-x
Nguyen KMedjaher KTran D(2022)A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelinesArtificial Intelligence Review10.1007/s10462-022-10260-y56:4(3659-3709)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1007/s10462-022-10260-y
Luo HBo LLiu XZhang H(2021)A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation TrajectoriesComputational Intelligence and Neuroscience10.1155/2021/25009972021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/2500997
Huang FSava AAdjallah KWang Z(2019)Data Piecewise Linear Approximation for Bearings Degradation Monitoring2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS.2019.8924384(60-64)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1109/IDAACS.2019.8924384
Xu GLiu MWang JMa YWang JLi FShen W(2019)Data-Driven Fault Diagnostics and Prognostics for Predictive Maintenance: A Brief Overview^*2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)10.1109/COASE.2019.8843068(103-108)Online publication date: 22-Aug-2019
https://dl.acm.org/doi/10.1109/COASE.2019.8843068
Kundu PChopra SLad B(2019)Multiple failure behaviors identification and remaining useful life prediction of ball bearingsJournal of Intelligent Manufacturing10.1007/s10845-017-1357-830:4(1795-1807)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10845-017-1357-8
Hong SYin J(2018)Remaining Useful Life Prediction of Bearing Based on Deep Perceptron Neural NetworksProceedings of the 2018 2nd International Conference on Big Data and Internet of Things10.1145/3289430.3289438(175-179)Online publication date: 24-Oct-2018
https://dl.acm.org/doi/10.1145/3289430.3289438
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