基于改进DBNs的三维叶尖间隙叶片裂纹诊断方法

Fault Diagnosis for Three⁃Dimension Blade Tip Clearance Based on Turbine Blade Crack by New Improved Deep Belief Networks

针对航空发动机结构复杂、干扰因素多、叶片裂纹特征提取困难及难以精确诊断等问题,提出一种基于改进深度信念网络(deep belief networks,简称DBNs)的三维叶尖间隙叶片裂纹特征提取与诊断方法。首先,根据DBNs重构误差的传递规律,通过全局反向重构(global back-reconstruction,简称GBR)机制构建一种能自适应调节深度的DBNs,以避免深层特征退化导致的特征表征能力不足的问题;其次,利用改进DBNs从叶片三维叶尖间隙中自适应学习深层裂纹特征;最后,采用Softmax回归模型建立深层特征与叶片裂纹间的复杂映射,实现叶片裂纹精确诊断。叶片裂纹诊断试验结果表明:所提方法能有效提取叶片裂纹特征,平均诊断精度达到98.43%,标准差仅为0.092%,具有较好的稳定性和泛化能力,能有效实现叶片裂纹诊断。

Due to the complex internal structure and multiple interference factors for aero-engine,it is difficult to extract features of the blade cracks and diagnose crack fault accurately.Hence,the paper proposes a new im-proved deep belief networks(DBNs)for feature learning,which is used to fault diagnosis for the blade cracks based on three-dimension blade tip clearance(3-DBTC).Firstly,based on the law of reconstruction error trans-mitting in hidden layers,the paper creates a new variant of DBNs to adaptively adjust the depth with global back-reconstruction(GBR)mechanism,aiming at avoiding feature degradation with an increase in depth of DBNs and then causing insufficient of the representation ability in features;Then,the variant of DBNs is adopt-ed to adaptively learn the blade crack deep-level features from the multi-dimensional signal 3-DBTC involving characteristic information of the blade cracks;Finally,the softmax regression model is used to build the com-plex mapping between the deep-level features and the blade cracks,accomplishing the accurate diagnosis for the blade cracks.The experimental results demonstrate that the proposed method can fully mine characteristic infor-mation of the blade cracks,and effectively improve the diagnosis accuracy,yielding average accuracy of 98.43%,and its standard deviation is only 0.092%,showing its stability and generalization ability are also pret-ty good and achieving effective fault diagnosis for the blade cracks.