Abstract
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.
Highlights
The fault diagnosis of gearboxes under varying operation conditions has attracted the extensive attention of researchers [1]–[4]
The basic idea of system characteristic diagnosis is that the system characteristic is an inherent property of the gearbox system and has nothing to do with the running state
When the system characteristics change, it indicates that the gearbox system is malfunctioning
Summary
The fault diagnosis of gearboxes under varying operation conditions has attracted the extensive attention of researchers [1]–[4]. L. Han et al.: Quantitative Diagnosis Method of Gearbox Under Varying Conditions Based on ARX Model and GCCA the characteristic information inside the system and directly reflects the dynamic causal relationship between the external variable groups of the system. A generalized canonical correlation analysis (GCCA) can highlight the nonlinear relationship between vectors, and can solve this contradiction It is more universal and more suitable for a fault diagnosis based on the system characteristics. Aiming at the problem of a non-linear quantitative identification of the ARX model, a nonlinear quantitative evaluation method based on GCCA is proposed to extract the non-linear correlativity of the system transfer function between gearbox features under a faulty status and those under a healthy status, and the quantitative evaluation results is expressed using the canonical correlation coefficients.
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