Time Series Multi-Channel Convolutional Neural Network for Bearing Remaining Useful Life Estimation

Juei En Lee, Jehn Ruey Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper proposes a deep learning method, called time series multi-channel convolutional neural network (TSMC-CNN), for remaining useful life (RUL) estimation of bearings. The time series data of bearing operation are divided into multiple channels to be fed into the convolutional neural network (CNN) to extract relationship between far apart data points. The PRONOSTIA bearing operation datasets are used to evaluate the proposed method performance. The evaluation results are compared with those of related methods to show the superiority of the proposed method in terms of the root mean squared error (RMSE) and the mean absolute error (MAE).

Original languageEnglish
Title of host publication2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
EditorsTeen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages408-410
Number of pages3
ISBN (Electronic)9781728125015
DOIs
StatePublished - Oct 2019
Event2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019 - Yunlin, Taiwan
Duration: 3 Oct 20196 Oct 2019

Publication series

Name2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019

Conference

Conference2019 IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2019
Country/TerritoryTaiwan
CityYunlin
Period3/10/196/10/19

Keywords

  • bearing
  • convolutional neural network
  • deep learning
  • remaining useful life

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