Identification of resonance states of rotor-bearing system using RQA and optimal binary tree SVM
Aiming to the dynamic nonlinearity of rotor-bearing system in the mechanical and fluid resonance states, a new method combining Recurrence Quantification Analysis (RQA) with optimal binary tree Support Vector Machine (SVM) is proposed for characterizing and identifying the resonance states. RQA is used to obtain the nonlinear characteristic parameters which are able to effectively represent the resonance states without large amount of measurement data. The binary tree SVM is ordered according to the rank of state Mahalanobis distances in the feature vector space. In order to more precisely classify the feature zones, the RQA features are optimally selected as the inputs for each classifier of binary tree SVM by means of the Fish score evaluation. The practical experiments are performed on the cylindrical shaft-journal bearing test rig and the results demonstrate the effectiveness and superiority of the proposed method.
resonance state rotor-bearing system binary tree svm recurrence quantification analysis