Vaughn College of Aeronautics and Technology

About: Vaughn College of Aeronautics and Technology is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Gravitational microlensing & Planetary system. The organization has 727 authors who have published 708 publications receiving 14082 citations. The organization is also known as: College of Aeronautics.

Initial Fault Diagnosis of Rolling Bearing Based on Support Vector Machine

Abstract: Aim at the problem of lacking fault data on aero-engine fault diagnosis,a rolling bearing initial fault diagnosis method based on support vector machine and wavelet packet is proposedThrough a finite learning samples,the function relationship between the initial fault character and it's running state is established based on Structural Risk Minimization,which is so-called faults classifierBy the faults classifier's output,the type of rolling bearing initial fault is determinedThe experiment shows that wavelet packet analysis can extract the initial fault character effectively and the support vector machine method can identify those initial faults of rolling bearing correctly

Adaptive Robust High-degree Cubature Filter Based On Target Tracking

Abstract: Target tracking system has strong-nonlinearity with non-Gaussian noise, thus making traditional Cubature Kalman Filter have low tracking accuracy due to sensitivity to non-Gaussian noise. Considering that the High-degree CKF and Maximum Correntropy Kalman Filter can conquer the nonlinearity of system and non-Gaussianity of measurement noise and system noise respectively, the measurement update process of HCKF is transformed to statistical linear regression equation based on the combination of Maximum Correntropy Criterion and HCKF, and adjust the kernel width adaptively, which solves the nonlinearity and non-Gaussianity problem. Simulation proves that the proposed robust generalized high-degree cubature filter is better than traditional CKF and simple MCKF both in tracing accuracy.

Research on Life Prediction of Airborne Fuel Pump Based on Combination of Degradation Data and Life Data

Abstract: The airborne fuel pump as a typical high-reliability, long-life electromechanical component, its life prediction has problems such as small sample size and low prediction accuracy. Firstly, the degradation testing platform for airborne fuel pump is designed to obtain degradation data and life data. Secondly, a drift wiener process degradation model based on the combination of degradation data and life data is established. Finally, the life prediction is performed. The results show that predicted life of the airborne fuel pump, by the drift wiener process degradation model based on the combination of degradation data and life data, is closer to the true value and has higher accuracy, comparing to that by drift wiener process degradation model based on degenerate data.

WNN soft fault diagnosis of analog circuit based on KPLS feature extraction

Abstract: In order to improve the ability of soft fault diagnosis of analog circuits, an integrated diagnosis method based on KPLS feature extraction and WNN was proposed. First, the good feature extraction ability of KPLS was used to construct the principal element feature set of fault sample set; then, the advantages of WNN on solving the complicated nonlinearity problems was applied to establish the fault identification model based on principal element feature set; finally, each failure mode was diagnosed and determined by the built model. The simulation experiment of Sallen-Key bandpass filter shows that the integrated method just completes the training of the model by less than 300 iterations computation with the total correct rate 96.7%, and the correct rate of 6 modes in 9 modes reaches 100%, which verifies its feasibility and effectiveness.