## 4 Experimental Verification
### Analysis of Test Results
It can be seen from Figure 11 that under the working condition of 1100 rpm, the diagnosis effect of the original fault data is poor; only the faults _xms_ and _vq0.6_ are separated from other faults, and the other faults have poor diagnosis effect, which is inferior to the NNF method. The classification effect is similar. AWF has a better diagnostic effect for each type of fault, but the aggregation of individual faults is poor, which makes each type of fault appear discrete. 1100 rpm test conditions Under the working condition of 1100 rpm, the classification results of the above eight types of faults using different methods are shown in Figure 12.  
Based on the classification of the original data, the classification effect of the NNF method is better. The fault data classification effect obtained by the STMF method is the best. Not only can the various types of faults be separated, but the aggregation degree of each type of fault is also better, which reflects the strong dynamic adaptability of the STMF method and greatly improves the accuracy of fault diagnosis. 1300 rpm test conditions Under the working condition of 1300 rpm, the classification results of the above eight types of faults using different methods are shown in Figure 13.  
Figure 12: Comparison of eight types of fault classification results under 1100 rpm. Under the working condition of 1300 rpm, in the original data, only \(n\) is separated, and the other fault data are divided into two piles and mixed together. In the fault diagnosis of AWF and NNF, only \(nq0.6\) and \(xms\) can be separated from other faults, and the rest are grouped together. In general, the fault diagnosis effects of OSD, AWF and NNF are not very satisfactory, resulting in several types of faults that cannot be separated. In the effect diagram of STMF diagnosis, although each type of fault looks relatively discrete, all types of faults have been separated. In contrast, the fault diagnosis effect of OSD, AWF and NNF is better, but the aggregation effect of each type of fault needs to be improved. In order to highlight the effectiveness of the STMF method, in addition to comparing the single sensor raw data, the traditional AWF data and the fault diagnosis classification graph of the NNF data, the fault diagnosis accuracy and the variance of the accuracy of each method are also counted. The establishment of the fault diagnosis accuracy and variance of each working condition and fusion method are shown in Figure 7:0  
It can be seen from Table 4 that under different speed conditions, the data obtained by the STMF method has the highest accuracy in the SAE fault diagnosis, and the accuracy of the diagnosis is very stable, so the variance of the diagnosis accuracy is very small, which is represented by a thin pink bar in Figure 7:1  
In comparison, the diagnostic accuracy of OSD, AWF, and NNF data is lower than that of the STMF method. The accuracy of data diagnosis of the NNF method is higher than that of AWF and OSD, but the accuracy variance is the largest among several methods. Yes, the stability is poor. Figure 12 shows the test times and accuracy graphs of several other methods. Figure 7:2 Comparison of eight types of fault classification results under 1300 rpm. STMF can be compared with other signal fusion methods to highlight the superiority of the fusion method. The STMF method has good dynamic adaptability because it is a weight distribution for the correlation analysis of real-time data. For any signal measured by any sensor, its weight will change with the support of other signals, so it has good dynamic adaptability. STMF method removes the noise from the original data through the wavelet transform method, which leads to the weight distribution of the method not beingaffected by the relevant noise, but only related to the support degree of other signals obtained by the measured useful signal. Therefore, the proposed method has good anti-interference performance.