Remaining Useful Life Prediction with Uncertainty Quantification Using Evidential Deep Learning

被引:0
|
作者
Ben Ayed, Safa [1 ]
Broujeny, Roozbeh Sadeghian [2 ]
Hamza, Rachid Tahar [2 ]
机构
[1] CESI LINEACT, EA7527, F-92000 Nanterre, France
[2] CESI LINEACT, EA7527, F-62000 Arras, France
关键词
Industry; 4.0; predictive maintenance; uncertainty; remaining useful life; evidential deep learning; PROGNOSTICS; NETWORKS; ENSEMBLE; LSTM;
D O I
10.2478/jaiscr-2025-0003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Predictive Maintenance presents an important and challenging task in Industry 4.0. It aims to prevent premature failures and reduce costs by avoiding unnecessary maintenance tasks. This involves estimating the Remaining Useful Life (RUL), which provides critical information for decision makers and planners of future maintenance activities. However, RUL prediction is not simple due to the imperfections in monitoring data, making effective Predictive Maintenance challenging. To address this issue, this article proposes an Evidential Deep Learning (EDL) based method to predict the RUL and to quantify both data uncertainties and prediction model uncertainties. An experimental analysis conducted on the C-MAPSS dataset of aero-engine degradation affirms that EDL based method outperforms alternative machine learning approaches. Moreover, the accompanying uncertainty quantification analysis demonstrates sound methodology and reliable results.
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页码:37 / 55
页数:19
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