A Deep Learning Approach for Data-Driven Predictive Maintenance of Rolling Bearings

Domicio Neto, Jorge Henriques, Paulo Gil, César Teixeira, Alberto Cardoso

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

Abstract

It is crucial for industrial companies that their systems are available and healthy as most as possible. However, it is inevitable that machines will degrade over time, leading to a fault, or even a complete breakdown if the fault is not identified and addressed in time. In this context, predictive maintenance, i.e., maintenance scheduled and implemented accordingly to the machine’s estimated condition and degradation, is considered a promising approach, as it can extend machines’ availability, productivity, overall product quality, and reduce the waste of material and human resources related to maintenance, among other benefits. It is, for this reason, a key object of Circular Manufacturing, which is an emerging discipline that aims at creating more clean and sustainable manufacturing environments. Deep Learning in this area has been increasingly researched, showing promising results and the ability to extract hidden and abstract information that can improve the performance of health status prediction. In this work, a predictive maintenance approach using Deep Learning is developed for the PRONOSTIA-FEMTO benchmark, regarding the prediction of the current health status of rolling bearing components. The dataset contains vibration data from several run-to-failure experiments. The preprocessing stage is carried out using local mean decomposition, enabling better feature extraction. The approach is then compared to another non-Deep Learning approach for performance assessment.

Original languageEnglish
Title of host publicationCONTROLO 2022
Subtitle of host publicationProceedings of the 15th APCA International Conference on Automatic Control and Soft Computing, July 6-8, 2022, Caparica, Portugal
EditorsLuís Brito Palma, Rui Neves-Silva, Luís Gomes
Place of PublicationCham
PublisherSpringer
Pages587-598
Number of pages12
ISBN (Electronic)978-3-031-10047-5
ISBN (Print)978-3-031-10046-8
DOIs
Publication statusPublished - 2022
Event15th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2022 - Caparica, Portugal
Duration: 6 Jul 20228 Jul 2022

Publication series

NameLecture Notes in Electrical Engineering
PublisherSpringer
Volume930
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference15th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2022
Country/TerritoryPortugal
CityCaparica
Period6/07/228/07/22

Keywords

  • Circular manufacturing
  • Health condition prognosis
  • Industrial systems
  • Machine learning
  • Predictive maintenance

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