Gearbox fault diagnosis based on a fusion model of virtual physical model and data-driven method

Planetary gear system is widely used in wind power, construction machinery and helicopters, because of its large transmission ratio, strong load-bearing capacity and small size. Because the gears are usually in high-speed operation, their key components are subject to severe wear and impact damage. When the gearbox is subjected to a failure, it is often difficult to judge the operating status of the gearbox in real time. Since the gearbox system is complex, moreover, it is still difficult for the simulation model to accurately identify the gearbox fault. To address this issue, this paper proposes a digital twin-based signal fusion model to accurately identify gearbox faults at the signal level. The proposed model can identify minor faults occurring in planetary gearboxes by fusing actual and virtual signals. Firstly, on the basis of virtual prototype and the multibody dynamics software, a digital physical model based on Hertz theory and extended finite element method is proposed for gearbox dynamic monitoring and digital simulation, in which the gearbox can be visualized. Secondly, the data-driven method is used to extract and select features from real and simulation signals, and the fault of the planetary gearbox is detected by feature matching. Finally, the real and simulation signals are fused effectively based on variational mode decomposition and phase correction of generalized cross-correlation coefficient. The fault diagnosis result of the gearbox is finally determined by using the envelope spectrum of the fused signal. The experimental results show that the virtual real fusion method effectively improves the performance of the traditional signal diagnosis methods, and the fusion of the actual and virtual signals strengthens the fault features of the fused signals effectively.