Learning methods for remaining useful life prediction in a turbofan engine

Abstract

In industry 4.0 there is a growth in the Industrial Internet of Things (iIoT) with a lot of information generation and consequent big data challenges. Thus, it is imperative to have techniques able to process all this data and predict the maintenance of equipment and systems. The development of algorithms for remaining useful life (RUL) estimators is critical for the full functioning of the company’s assets. Especially the aeronautical sector needs to guarantee safety and quality flights. The turbofan, a propulsion engine, is a critical element for an airplane operation. This paper proposes a model to perform prediction of the remaining useful life of an aircraft’s turbo engine. In this work, we focus on the run-to-failure data from an N-CMAPSS turbofan, the data used were provided by NASA in 2021. After training and validating different algorithms such as MLP and CNN, we find CNN as the best approach with an RMSE of 9.11, a score of 5.14, and computed score of 1.17. The results have improved when compared to the literature over 25% in RMSE and 15% in computed score.

Long-Short-Term Memory Model for Remaining Usable Life Estimation

“…The data is pre-processed, normalized, and a sliding window is used to scroll through selected feature data. The performance evaluation of this proposed model is conducted based on Mean Absolute Error (MAE) and RMSE, the RMSE values for MLP and CNN are 11.67, and 9.11 respectively[3]. The comparative evaluation shows the accuracy of the proposed model is better than other ML algorithms reported in the literature.…”