Abstract
Remaining useful life estimation (RUL) is the remaining time until the system failure. Predicting RUL help to schedule the maintenance actions in advance which can improve the reliability and availability of industrial systems while reducing the downtime and maintenance cost. In this paper, a deep ensemble approach for RUL estimation is developed, where the RUL is predicted with two different models: convolutional neural network which is suitable for achieving high level automatic features extraction, and long short term memory is able to capture the temporal information in time series data. The predicted RULs by each model are then aggregated using a weighted mean fusion. The proposed approach is validated using degradation data generated from aircraft engines (C-MAPSS dataset), it can improve the reliability of prediction as well as the accuracy, where it showed promising performance results comparing with the related works in the state of the art.
Supported by the European Union, European Regional Development Fund.
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This paper is the result of the research work supported by the European Union, European Regional Development Fund.
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Abid, K., Sayed-Mouchaweh, M., Cornez, L. (2021). Deep Ensemble Approach for RUL Estimation of Aircraft Engines. In: Hasic Telalovic, J., Kantardzic, M. (eds) Mediterranean Forum – Data Science Conference. MeFDATA 2020. Communications in Computer and Information Science, vol 1343. Springer, Cham. https://doi.org/10.1007/978-3-030-72805-2_7
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