## 4 Experimental Verification
### Analysis of Test Results  
The maximum speed that the test motor can reach is 1500 rpm, and different motor speeds are set in the test, which are 700 rpm, 900 rpm, 1100 rpm, 1300 rpm. Among them, 1300 rpm is the rotation speed closest to the resonance frequency of the motor. The test working conditions are set to observe whether the fault results will change differently here. The test condition of 1500 rpm is too dangerous, so there is no need to set it. The SAE fault diagnosis method is used to compare and verify the original data and fusion data of each working condition. In addition, the traditional AWF and NNF methods are compared with the STMF method, which highlights the superiority of STMF data. When the fault data are verified, the fault type has normal data (\(n\)), bearing fault, planetary gear fault. The types of bearing faults are included 0.6 mm cracks in the inner ring (\(n\)\(q\)\(0.6\)) and 0.6 mm cracks in the outer ring of the bearing (\(w\)\(q\)\(0.6\)). The types of planetary gear fault include planetary gear crack 1.2 mm fault (\(dlw\)), planetary gear crack 3.6 mm fault (\(x\)\(l\)\(w\)), planetary gear pitting fault 3.6 mm (\(6\)\(ds\)), planetary gear wear 1.2 mm fault (\(x\)\(ms\)), planetary gear wear 3.6 mm fault (\(d\)\(ms\)). In Figures 10-13, the dimension1 of the horizontal coordinate refers to the fault feature 1 after dimensionality reduction, and the dimension 2 of the longitudinal coordinate refers to the fault feature 2 after dimensionality reduction. Figure 8: Gear failure. Figure 7: Bearing fault. 1. 700 rpm test conditions Under the working condition of 700 rpm, the classification results of the above eight types of faults using different methods are shown in Figure 10.  
Figure 10: Comparison of eight types of fault classification results under 700 rpm. Figure 9: Time domain and envelope spectrum graphs with signals of different health conditions. The OSD, AWF, NNF and STMF data were verified by the fault diagnosis method of SAE. It can be seen that, when the motor speed is 700 rpm, only three types of fault in the original data of a single sensor are well separated in OSD, while the other five types of fault are all mixed together, and the classification effect is poor. In comparison, the classification effect of the data obtained by the AWF is better. However, the clusters of each type of failure do not aggregate well. Compared with the first two kinds of data, the data of NNF are easier to be classified and clustered. The proposed STMF method can determine the weight in the case of direct correlation analysis of real-time data, without any prior knowledge of sensor measurement data and the influence of some external factors, which is easy to implement. The fault diagnosis classification graph obtained by the STMF method is not only good for each type of fault, but also for the compactness of each type of fault. 900 rpm test conditions  
Under the working condition of 900 rpm, the classification results of the above eight types of faults using different methods are shown in Figure 11.  
The classification effect of OSD is average, and several types of faults are clustered together under the working condition of 900 rpm. The aggregation degree of each fault in the AWF method is relatively poor, and the classification results of several faults are not very good; only _xms_ and _nq0.6_ are separated from other faults. In NNF, the four types of fault classification results of planetary gear cracks (1.2 mm and 0.6 mm) and wear and bearing inner ring cracks of 0.6 mm are poor, all of which will affect the accuracy of the final fault diagnosis. Figure 11: Comparison of eight types of fault classification results under 900 rpm. In STMF fault diagnosis, in addition to large cracks in the planetary gear and poor normal classification, other faults are well classified, and the aggregation effect of each fault is also excellent, which greatly improves the accuracy of fault diagnosis.