涡轮叶片早期裂纹的三维叶尖间隙EEMD能量熵融合诊断方法

为了解决航空发动机涡轮叶片早期裂纹故障信号微弱、难以识别的问题,提出一种基于三维叶尖间隙集成经验模态分解（EEMD）能量熵融合的涡轮叶片早期裂纹诊断方法。采集涡轮叶片三维叶尖间隙信息,利用EEMD分别对三维叶尖间隙各维信号进行处理,得到相应的固有模态函数(IMF),以此计算每一维信号分量EEMD能量熵,构建能表征叶片裂纹状态的不同EEMD能量熵高维矢量集。建立多个堆叠自动编码器(SAE)分别对各高维矢量集进行特征学习并提取所学习的深层特征表达。利用支持向量机算法(SVM)和遗传算法(GA)融合各维深层特征以综合不同维度信息进而充分判定叶片裂纹状态。通过涡轮叶片裂纹诊断试验,结果表明:所提方法能有效提高叶片早期裂纹诊断精度,其平均准确率达到98.415%,标准差仅为0.697%,具有很好的稳定性、泛化性和自适应性。

Approach to early crack diagnosis of turbine blade based on EEMD energy entropy fusion of three-dimensional tip clearance

Considering that the early crack signal of aero-engine turbine blade is weak and difficult to identify, a diagnosis method was proposed based on ensemble empirical modal decomposition (EEDM) energy entropy fusion of three-dimensional tip clearance. Three-dimensional tip clearance signals of turbine blade were collected and decomposed by using the EEMD to obtain corresponding three-dimensional intrinsic mode functions (IMFs) and calculate the EEMD entropy value of each dimensional signal in order to form different high-dimensional vector sets representing the crack fault. Several stacked autoencoders (SAE) were constructed to learn the high-level features from each high-dimensional vector set and further extract the in-depth feature expression acquired by each SAE. The support vector machine (SVM) algorithm and genetic algorithm (GA) were used to fuse these different high-level features in order to confirm adequately the early crack fault of turbine blades. Results show that the proposed method can fully dig out characteristic information of the early crack, and effectively improve the diagnosis accuracy, yielding an average accuracy of 98.415%, and its standard deviation is only 0.697%, showing its good stability, adaptivity and generalization ability.