Aeroengine Lubrication Monitoring Analysis Via Support Vector Machines

A novel aeroengine lubrication monitoring method based on support vector machines is presented in this paper.Basic theory analysis of support vector regression in time series forecasting is introduced in detail and a multi-step forecasting formula is presented,Final Prediction Error (FPE) principle is suggested to select the embedding dimension.Compared with general autoregressive forecasting method it adopts new type of structural risk minimization principle and thus it owns excellent generalization ability.During numerical simulations,we infer that Auto-Regressive (AR) forecasting method is suitable to short intervals while Support Vector Machines (SVM) still possesses good robustness and fault-tolerant virtue in metaphase intervals forecasting.Finally,some typs of aeroengine's lubrication metal content have been monitored for feasibility validation and test results are satisfactory.