Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework

Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and attention in the field fault diagnosis. However, similar component reorganization problem involved this method not been clearly stated. Aiming at problem, paper proposes SGMD-CS based on SGMD cosine similarity (CS) compared verified simulation actual rolling bearing signal. In addition, order to realize intelligent diagnosis wind turbine fault, symplectic entropy (SymEn) extracted as feature input into AdaBoost classification model. summary, new extraction framework, validity by vibration data Electrical Engineering Laboratory Case Western Reserve University.