A Data-driven Approach for Remaining Useful Life Prediction of Aircraft Engines

It is crucial to predict the remaining useful life (RUL) of aircraft engines accurately and timely for the aircraft operation safety and appropriate maintenance decision. The key issue is how to efficiently mine the internal relation hiding in historical time series monitoring data with high dimension features. In this paper, a data-driven prediction method is proposed by combining the time window (TW) and extreme learning machine (ELM). First, based on the specific properties of aircraft engines time series data, a sliding time window is introduced to sample the historical data to obtain the input vector. Then, the extreme learning machine is utilized to model the relation between time series data and RUL. The proposed approach is validated on the turbofan data sets widely employed by other literatures. Experimental results verify the prediction accuracy and efficiency of the proposed approach compared with the existing methods.