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A Remaining Useful Life Prediction Framework for Aero-engine Using Information Entropy-based Criterion and PCA-RVM

To deal with the challenge of feature selection and extraction in the remaining useful life (RUL) prediction for aero-engines, this paper proposes a framework using multi-sensors data, which involves three key components (i) an information entropy-based criterion for sensor selection, (ii) principal component analysis (PCA) for the construction of synthesized health index, and (iii) relevance vector machine (RVM)-based RUL prediction. The proposed method combines the PCA with RVM and improves the prediction accuracy by employing a novel entropy-based criterion for sensor selection. The effectiveness of this approach is demonstrated and validated with the turbofan engine released by NASA Research Center.