Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems

N. Borst, W. J.C. Verhagen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Prognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access, 2019, 7, pp 75464-75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications.

Original languageEnglish
Pages (from-to)2143-2153
Number of pages11
JournalAeronautical Journal
Volume127
Issue number1318
DOIs
Publication statusPublished - 2023

Keywords

  • Neural networks
  • Predictive maintenance
  • Prognostics
  • Remaining useful life

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