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| Data-driven models have been widely used to predict the remaining useful life (RUL) of many engineering systems, e.g. aircraft engines. However, two shortcomings exist: (i) single algorithm has performance limitations for the specific application and (ii) reliably tracking the degraded performance of aircraft engines remains challenging. In this paper, a new ensemble learning prognostic method is proposed, which considers the effects of performance degradation on RUL. First, the overall degradation process is divided into multiple degradation stages, which present the performance of aircraft engines. Then, in each degradation stage, the higher prediction accuracy the base learner obtains, the higher weight is assigned to the base leaner. Finally, based on the obtained weights, the predicted results of all base learners are combined to predict the RUL of aircraft engines. The experimental results of aircraft engines verify the effectiveness and practical value of the proposed method. The results show that the proposed method has a good predictive effect and fits the degradation curve of the aircraft engines well. |
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| Keywords:General industrial technology; Reliability; Ensemble learning; Remaining useful life; Aircraft engine; prognostics |
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