Quantitative Diagnosis Method of Gearbox Under Varying Conditions Based on ARX Model and Generalized Canonical Correlation Analysis

Fault diagnosis of gearboxes under the condition of varying speed and varying load is a hotspot and difficulty in the research of gearboxes. The response signals of gearbox under varying conditions exhibit non-linear and non-stationary characteristics, which increase the complexity of quantitative diagnosis of gearbox faults. A quantitative diagnosis method of gearbox faults based on the improved autoregressive with exogenous (ARX) model and generalized canonical correlation analysis (GCCA) is proposed in this paper. The ARX model is improved based on incremental recursive identification of Kalman filter to build system transfer characteristic models using the excitation and response signals of gearboxes. ARX models of gearboxes are nonlinear and the GCCA is proposed to build the quantitatively relationship between models with faulty status and healthy status. Simulation and experiment results indicate that the proposed method can effectively identify the severity of the gearbox failures under varying conditions and provides a promising method for the quantitative diagnosis of rotating machinery.