Application of Fuzzy-Rough Set theory and improved SMO algorithm in aircraft engine vibration fault diagnosis

Aircraft engine is an important component of the airplane, which lifespan and reliability have directly influence to flight safety. Vibration analysis is an effective fault diagnosis method for airplane's mechanical construction fault. Several typical vibration faults have drawn from the vibration analysis on the basis of fault mechanism in aircraft engine, and the standard vibration fault-symptom (Fuzzy Membership) relationship table have constructed based on Fuzzy Mathematic theory in this paper. Sample data are simulated on the basis of this table. Sequential Minimal Optimization (SMO) is a fast algorithm for training Support Vector Machines (SVM). The simulated result showed a great improvement on computation efficiency by selecting double threshold parameter for SMO algorithm than single threshold. In order to examine the algorithm's effect applying in the aircraft engine fault diagnosis, Radial Basis Function (RBF) Neural Network, C4.5 Decision Tree, SVM (chunking), Platt's SMO and Improved SMO algorithm are applied to the training data and test data. It is shown that the Improved SMO algorithm displayed superior comprehensive performance. Finally, the vibration fault feature is reduced based on Fuzzy-Rough Sets theory. The research shows that the Improved SMO algorithm is quicker than other algorithm in aircraft engine fault diagnosis with the reduced feature attributes, and its accuracy is high.