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摘要:
针对传统相似性匹配方法易引入伪相似发动机导致预测精度不高的问题，提出一种结合发动机性能退化特点与相似性匹配特点的区间划分改进相似性剩余寿命(RUL)预测方法。利用堆栈自编码器基于选择的参数构建健康指数；根据测试发动机的已知运行循环进行区间划分，利用传统相似性匹配方法进行初步筛选；对于不同区间下的测试发动机，分别使用不确定性修正和退化一致性检验去除初选参考发动机中的异常发动机，得到最终的剩余寿命预测结果。利用NASA的C-MAPSS数据集进行验证，结果表明：所提方法的预测精度比当前相似性匹配方法提升了34%，证明了方法的有效性。
Abstract:The traditional similarity matching method is liable to introduce pseudo-similar engines, which leads to low prediction accuracy. To address this issue, a remaining useful life (RUL) prediction method of improved similarity with interval partition was proposed, which combined the engine performance degradation characteristics and similarity matching characteristics. Firstly, the health index was constructed based on the selected parameters by using the stacked autoencoder. Then, based on the known running cycles of the test engine, the interval was divided, and the traditional similarity matching method was used for preliminary screening. For the test engines in different intervals, the uncertainty correction and degradation consistency test were performed to remove the abnormal engine from the preliminarily selected reference engines, and the final remaining life prediction results were obtained. The C-MAPSS dataset of NASA was used for verification, and the results show that the prediction accuracy is 34% higher than the current similarity matching method, which proves the effectiveness of the proposed method.
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图 3 区间划分改进相似性方法流程
Figure 3. Flow of improved similarity method with interval partition
表 1 堆栈自编码器网络参数
Table 1. Stacked autoencoder network parameters
层数 节点数 激活函数 1 4 Sigmoid 2 2 Sigmoid 3 1 Sigmoid 
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表 2 区间划分改进相似性参数
Table 2. Improved similarity parameters with interval partition
参数 数值 时间窗口长度$ W $ 60 距离放缩因子$ \lambda $ 0.002 相似滤波系数$ \gamma $ 0.9 多模型个数$ N $ 20 时间窗口平移系数$ \beta $ 1 伪相似度百分比$ \theta $ 80
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表 3 第89台测试发动机及对应的伪相似度
Table 3. The 89th test engine and its pseudo-similarity
第89台测试发动机
RUL真实值初选参考
发动机序号初选参考发动机
RUL预测值传统相似性匹配方法 退化一致性检验 相似度 相似度阈值 RUL预测值 误差 伪相似度 伪相似度阈值 RUL预测值 误差 28 20 27 0.985 0.906 42 14 1.018 1.234 32 4 54 20 0.988 1.100 59 26 0.997 1.217 80 62 0.991 1.389 91 62 0.992 1.388 52 26 0.992 1.202 49 29 0.987 1.485 95 61 0.994 1.476 $ \vdots $ $\vdots $ $\vdots $ $\vdots $
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