基于退化特征相似性的航空发动机寿命预测

针对航空发动机结构复杂、性能退化参数众多、寿命预测精度低等问题,提出了一种基于退化特征相似性的寿命预测方法。首先通过基于Relief算法的退化特征筛选、基于主成分分析(principal component analysis,PCA)的特征提取和基于核函数的特征平滑,提取低维正交多变量退化特征;然后进行特征的相似性匹配,寻找与当前样本特征片段最相似的一组历史样本中的特征片段集合,将这些片段对应的RUL信息融合并采用密度加权方法得到当前样本的寿命预测估计值;最后通〖JP2〗过美国国家航空航天局(national aeronautics and space administration,NASA)提供的航空涡轮扇发动机仿真数据集验证了该方法的有效性,其寿命预测性能高于现有几种代表性方法。

Remaining useful life prediction for aero engine based on thesimilarity of degradation characteristics

Aircraft engines are with highly complex structures, a large number of performance parameters monitored and low remaining useful life (RUL) prediction accuracy in their degradation processes. A method based on the similarity of degradation characteristics is proposed for predicting the RUL of aircraft engines. Firstly, the Relief algorithm is used for feature selection, principal component analysis is used for feature extraction, and the Kernel algorithm is used for feature trajectory smoothing. Through this, a few low dimensional orthogonal degradation characteristics are obtained for RUL prediction. Then, a similarity based matching algorithm is adopted to find out a group of similar degradation segments in the referenced samples, and the corresponding RUL information of these degradation segments are integrated via a density weighting method to obtain the final RUL prediction of the current sample. Finally, the proposed method is evaluated by using the turbofan engine degradation simulation data announced by national aeronautics and space administration (NASA), and the results can show its superiority compared with several popular RUL prediction methods.