# Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG
## 4 Experiments  
### Experimental Data Acquisition  
To verify the effectiveness of the proposed method, a gearbox fault detection experiment was set up using an existing gear test bench of the experimental team. The first step was the design of the gearbox experiment, the reasonable selection of sensor positions, and gear selection. The sensor selection for the three three-phase sensors allowed comprehensive monitoring of the gears in the three dimensions of the gearbox. The experimental design is shown in Figure 9. The experimental conditions were established after completion of the design, as shown in Figure 10. This present study aims to make clear the detection of pinion gear teeth under different degrees of pitting, mainly detecting gear teeth in  
Figure 8: Comparison before and after the fusion of machine learning and deep learning. (**a**) BP algorithm multi-sensor fusion. (**b**) GA-BP algorithm multi-sensor fusion. (**c**) VGG16 algorithm multi-sensor fusion. normal condition, and exhibiting slight pitting and medium pitting. The pitting area of the intermediate wheel teeth in the case of slight pitting was 13.09% of the surface area of the wheel teeth, and the pitting area of the two adjacent wheel teeth was 4.36% of the surface area of the wheel teeth. The pitting area of the intermediate wheel teeth in the case of medium pitting was 26.18% of the surface area of the wheel teeth, the pitting area of the two adjacent wheel teeth was 13.09% of the surface area of the wheel teeth, and the pitting area of the two adjacent wheel teeth was 4.36% of the surface area of the wheel teeth [31]. All pitting craters were 1 mm in diameter and 0.5 mm in depth, and the pitting occurred near the pitch line, with a surface area of engagement of 65.70 mm\({}^{2}\). Thus, for the slight pitting fault, there were 11 pits on one gear teeth and four pits on the two adjacent gear teeth. In terms of the medium pitting fault. There were 22 pits on one gear tooth, 11 pits on the two adjacent gear teeth, and four pits on the two adjacent gear teeth. The pits were located near the knuckle line. The schematic diagram of gear pitting is shown in Figure 11, the actual gear health state is shown in Figure 12, and the experimental data selection is shown in Table 7.  
Figure 9: Structural design of the experimental gear. Figure 11: The pitting damage levels from slight to medium on the nth tooth and its neighboring teeth: (**a**) slight pitting fault; (**b**) medium pitting fault. Figure 12: Gear tooth health level. (**a**) Normal condition. (**b**) Slight pitting condition. (**c**) Medium pitting condition. Figure 10: Experimental setup 1,7—active motor, 2,5—coupling, 3,6—torque speed sensor, 4—gearbox, 4.1—drive gear, 4.2—driven gear, 4.3–4.5—three-phase acceleration sensor. A low load was applied to the pinion gear teeth for fault detection at rotational speeds of 500 and 600, as required by the partner company. The gear load varied with the degree of pitting. Under normal conditions, the load was 0 N \(\cdot\) m. The load increased in steps of 5 N \(\cdot\) m to 30 N \(\cdot\) m. According to the experiments, slight pitting took place at a speed of 600 r/min and a load of 10 N \(\cdot\) m. On the basis of multiple sets of experiments, it was found that pitting took place at a speed of 600 r/min and a load of 10 N \(\cdot\) m. Moderate pitting took place when the rotational speed was 600 r/min and the load was 30 N \(\cdot\) m. Therefore, the experimental data are all at 600 r/min, the load of the normal condition is 0 N \(\cdot\) m, the load of slight pitting fault is 10 N \(\cdot\) m, and the load of medium pitting fault is 30 N \(\cdot\) m.  
The experimental data for the gear teeth were collected by selecting the experimental data for a certain direction in which the fault state was most obvious from the three sensors.