Remaining Useful Life Prediction with Uncertainty Quantification Using Evidential Deep Learning

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.