Fault detection and classification of the rotor unbalance based on dynamics features and support vector machine

Rotor unbalance faults are one of the high-frequency faults in rotating machinery. As such, their accurate and timely diagnosis is important. In contrast to traditional methods based on static features, the dynamics features and support vector machines (SVM) are combined for the accurate detection and classification of rotor unbalance faults. First, the dynamical trajectories of the rotor system associated with unbalance faults are accurately identified locally based on the deterministic learning theory, which is more sensitive to abnormal changes in the rotor system. Second, entropy dynamics features, including the sample entropy, fuzzy entropy, and permutation entropy, are extracted based on the obtained dynamical trajectory data. Finally, the dynamics features are used to train the fault classifier based on the SVM with a Gaussian kernel function. Experiments on a rotor unbalance fault test rig demonstrate the effectiveness of the proposed method. The accurate detection and classification of rotor unbalance faults were also achieved compared with the results of employing static time or frequency features.