## 5 Experimental Results
### DBN Training and Test Results Analysis  
Then, obtain the trend feature vectors \(I_{x}=\left[\begin{array}{cccc}I_{x\_1}&I_{x\_2}&\cdots&I_{x\_24}\end{array} \right]\), \(x=1,2,\cdots,22\), where \(x\) denotes the failure coding number. The DBN consists of double-layer RBM and Softmax classifier. The number of neurons in the first layer of RBM is set to 14, and the number of neurons in the second layer of RBM is set to 5. The output of Softmax classifier is a 22 dimensions probability vectors \(S=\left[\begin{array}{cccc}S_{1}&S_{2}&\cdots&S_{22}\end{array}\right]\), where \(S_{j}(j=1,2,\cdots,22)\) denotes the probability of the \(j\)-th fault. Table 8 shows the probability output results of Softmax classifier and corresponding failure coding number.  
Table 8 shows that, for instance, put the No. 1 trend feature vectors into DBN model, the classification result is \(S=[0.998857,\,0.000000,\,\ldots,\,0.00000]\), which means the probability of No. 1 trend feature vectors belonging to No. 1 state is 99.999%. Put the No. 22 trend feature vectors into DBN model, the classification result is \(S=[0.000000,\,0.000080,\,\ldots,\,0.999353]\), which means the probability of No. 22 trend feature vectors belonging to No. 22 state is 99.9997%. Table 8 verifies the simulation classification results generated from the proposed method are accurate to the failure coding number, if the accurate standard is upper than 50%.  
Figure 8: Waveforms of phase-A current and the mode coefficients serials under No. 2 failure state. **(a)** Waveform of phase-A current. **(b–h)** Waveforms of mode coefficients serials from 1 st level to 7 th level.  
The following conclusions can be analyzed and drawn from Table 9:  
1. The method of VMD-TFA-DBN, proposed in this paper, has generated the best classifying capability under the 22 circumstances, with an accuracy of 99.99%, and the error times is 3.
2. The method of only BP has produced the worst classifying performance, the accuracy is 1.37%, the error times is 433971.
3. When the accuracy of VMD-TFA-DBN is higher than VMD-TFA-BP, TFA-DBN is higher than TFA-BP, VMD-DBN is higher than VMD-BP, and DBN is higher than BP. All of these illustrate the classification accuracy of DBN in higher than BP in the models.
4. The accuracy of each method used proposed TFA is higher than corresponding who does not use TFA, which verifies the great function of TFA for increasing classification accuracy.
5. The accuracy of each method used VMD is higher than corresponding who does not use VMD, which verifies the function of VMD in proposed method.
6. The classical method WT-FA-JD-BP has generated the second best performance, as the accuracy is 99.99%, and the error times is 5.  
The conclusions of experimental results are broadly in line with what of simulation results. But the performance of each method of experimental results is worse than corresponding method. The probable causes are summarized as follows:  
1. Three-phase current is extracted with error or interference. The samples are varying to indistinct, which lead to the accuracy decreased.
2. The total number of experimental samples in training set may be lack of, which leads to the DBN training model leaky.