Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach

Tae San Kim, So Young Sohn

Research output: Contribution to journalArticle

Abstract

Predicting remaining useful life (RUL) is crucial for system maintenance. Condition monitoring makes not only degradation data available for RUL estimation but also categorized health status data for health state identification. However, RUL prediction has been treated as an independent process in most cases even though potential relevance exists with health status detection process. In this paper, we propose a convolution neural network based multi-task learning method to reflect the relatedness of RUL estimation with health status detection process. The proposed method applied to the C-MAPSS dataset for aero-engine unit prognostics supported superior performances to existing baseline models.

Original languageEnglish
JournalJournal of Intelligent Manufacturing
DOIs
Publication statusAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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