LASSO based variable selection for similarity remaining useful life prediction of aero-engine
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摘要:
由于航空发动机监测变量众多，传统方法直接选取性能退化趋势明显的变量进行寿命预测，所以提出一种基于LASSO（least absolute shrinkage and selection operator）的变量选取方法，结合相似性寿命预测方法有效提高了预测精度。基于K-means聚类区分不同工况，对航空发动机多个监测变量根据聚类结果进行变量转换。基于LASSO方法选取最优传感器变量。基于相似性方法进行航空发动机剩余寿命预测。将基于LASSO的变量选取方法与传统的根据退化趋势大小进行选择的方法进行剩余使用寿命预测的结果进行了对比研究。结果表明：基于LASSO选取变量的相似性寿命预测误差的标准差在3种运行周期下分别减少了约1.84、3.46、4.23。
Abstract:Due to the large number of aero-engine monitoring variables, the variables with obvious performance degradation trend were directly selected by traditional method for the life prediction, so a variable selection method based on LASSO (least absolute shrinkage and selection operator) was proposed, which combined with the similarity life prediction method to effectively improve the prediction accuracy. Based on K-means clustering, different working conditions were distinguished, and multiple monitoring variables of aero-engine were transformed according to the clustering results. The optimal sensor variables were selected based on the LASSO method. The remaining useful life of aero-engine was predicted based on similarity method. The results of remaining useful life prediction based on the variable selection method by LASSO and the traditional selection method by the degradation trend were compared. The results showed that the standard deviation of the similarity life prediction error based on the variables selected by LASSO decreased by about 1.84, 3.46 and 4.23 under three operating cycles.
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Key words:
- prognostics health management /
- K-means clustering /
- LASSO method /
- similarity /
- remaining useful life
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图 6 90%运行周期的数据拟合概率分布（传统方法）
Figure 6. Data fitting probability distribution of 90% operation cycle （traditional method）
图 7 90%运行周期的数据拟合概率分布（LASSO方法）
Figure 7. Data fitting probability distribution of 90% operation cycle （LASSO method）
表 1 6个聚类中心参数表
Table 1. Parameter table of 6 cluster centers
聚类中心 Opsetting_1 Opsetting_2 Opsetting_3 1 10.0030 0.2505 20 2 25.0030 0.6205 80 3 20.0029 0.7005 0 4 35.0030 0.8405 60 5 0.0015 0.0005 100 6 42.0030 0.8405 40 
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表 2 两种方法预测误差的标准差结果
Table 2. Standard deviation results of prediction errors of the two methods
运行时间 方法 预测误差的标准差 50%周期 LASSO选取方法 24.6605 传统方法 26.4961 70%周期 LASSO选取方法 16.6148 传统方法 20.0720 90%周期 LASSO选取方法 13.8048 传统方法 18.0313
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