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

Tae San Kim and So Young Sohn ()
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Tae San Kim: Yonsei University
So Young Sohn: Yonsei University

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 8, No 6, 2169-2179

Abstract: 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.

Keywords: Prognostics and health management; Remaining useful life; Multi-task learning; Convolution neural network (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10845-020-01630-w

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