Feature Extraction of Kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning

The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.

Blind false data injection attacks in smart grids subject to measurement outliers

False data injection attacks(FDIAs)can manipulate measurement data from Supervisory Control and Data Acquisition(SCADA)system and threat state estimation in smart grids.Blind FDIAs(BFDIAs)enhance traditional FDIAs,which eliminate the limitation of grasping measurement Jacobian matrix H in advance,but when there are outliers in measurement data,attack performance is degraded.In this paper,improved BFDIAs are proposed.In off-line phase,lowdimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm(LLTSA)is sent into Continuous Deep Belief Network(CDBN)as training data to learn their probability distribution.In on-line phase,real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs,and outputs are reconstructed by the probability distribution in off-line phase,which eliminates the influence of outliers indirectly.Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.