Abstract：A fault diagnosis method based on local linear embedding (LLE) is proposed in the paper to deal with turbine rotor fault, which is constructing a feature matrix with original vibration signals as the input data of manifold learning, and embedding the high-dimensional data into low-dimensional space, by LLE algorithm for dimensionality reduction. The rate of fault diagnosis is calculated by cloud neural network when the output dimensionality of LLE algorithm is greater than 3, and the relationship between the rate and parameters is discussed in this paper. Although the sample of fault is limited, we are success to complete the fault diagnosis with the method which is represented in this paper.
孙 斌 薛广鑫 陈 军 蒋能飞. 基于局部线性嵌入和云神经网络的转子故障诊断方法[J]. , 2012, 31(23): 99-103.
SUN Bin;XUE Guang-xin;CHEN Jun;JIANG Neng-fei. A Method of Rotor Fault Diagnosis Based on Local Linear Embedding and Cloud Neural Network. , 2012, 31(23): 99-103.
2012, Vol. 31 
