Remaining useful life (Rul) prediction of equipment in production lines using artificial neural networks

Ziqiu Kang, Cagatay Catal, Bedir Tekinerdogan*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.
Original languageEnglish
Article number932
Pages (from-to)1-20
Number of pages20
JournalSensors (Switzerland)
Volume21
Issue number3
DOIs
Publication statusPublished - 30 Jan 2021

Keywords

  • Data mining
  • Machine learning
  • Maintenance prediction
  • Predictive maintenance
  • Production lines

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