Deep Learning for Improved System Remaining Life Prediction

Jianjing Zhang, Peng Wang, Ruqiang Yan, Robert X. Gao

Research output: Contribution to journalConference articlepeer-review

46 Scopus citations

Abstract

Prognosis of manufacturing system performance degradation is essential to operation safety and efficiency, and provides the basis for predictive maintenance scheduling. Complex physical mechanisms underlying machine operations often impose challenges to accurate health state assessment. This paper presents a data-driven approach to tracking system state degradation and consequently, predicting the remaining useful life, based on the Long Short-Term Memory (LSTM) network. Using aircraft engine fleet as an application context, the developed method reveals the temporal-dependency embedded in sensor data streams as the basis for engine degradation prediction. Good performance in engine remaining useful life prediction is demonstrated.

Original languageEnglish
Pages (from-to)1033-1038
Number of pages6
JournalProcedia CIRP
Volume72
DOIs
StatePublished - 2018
Event51st CIRP Conference on Manufacturing Systems, CIRP CMS 2018 - Stockholm, Sweden
Duration: May 16 2018May 18 2018

Keywords

  • Deep Learning
  • Degradation Prognosis
  • Engine Fleet

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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