# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 3 Experiments
### Experiment Result  
This paper first conducts a single-source domain adaptation experiment to verify the effectiveness of the composite model. This paper takes the working conditions (bearing loads) as the domains, trains on 10 kinds of fault modes data under each load condition, and then tests the trained model under another load condition. The data from each domain is divided into a training set and a test set at a ratio of 8.2. For each iteration during training, the model is first trained with the training sets from the source and target domains, and then the test set from the source and target domains are used to test the performance of the model and observe the classification accuracy of the model on the source and target domains. The number of training iterations is 100 and repeated 10 times. The classification result is the average of the optimal classification accuracy on the target domain. Considering the noise-resistant requirement of the model, the dataset selects the signal containing noise for experiments. The 2 DCNN network as the benchmark model still has a classification accuracy of more than 99% on the noisy signal with SNR = \(-4\), and the accuracy will decrease on the noisy signal with a lower signal-to-noise ratio. In order to ensure that the models participating in the experiment have a relatively high accuracy in the source domain so as to compare their domain adaptation capabilities, a noisy signal with SNR = \(-4\) is selected for cross-domain experiments. Among the methods for comparison, the classical CNN is first selected as the benchmark model to show the transfer performance of the model without any transfer method. Feature-based domain adaptation methods are then selected for comparison, represented by DAN using the MK-MMD distance and DSAN using the LMMD distance. In addition, adversarial-based domain adaptation methods are also selected for comparison, represented by DANN networks. The last is a composite model that combines the DANN and MK-MMD distance. The classification accuracy of each method under cross-working conditions is shown in Table 2. The left side of the arrow represents the source domain, and the right side represents the target domain. The accuracy shown in the table is the classification accuracy of the model on the target domain. The accuracy on the source domain is close to 100%, so it is not shown in this table. The results in Table 2 show that considering the CNN without any optimization and domain adaptation methods as the benchmark, the average accuracy of the CNN model on the unlabeled target domain is 87.08%. However, DAN and DSAN, which use the distance method for domain adaptation, both perform mediocrely. DAN only outperforms the CNN model on a few cross-domain tasks, with no advantage in average accuracy. The DSAN method is even negative optimization, which is due to the fact that the image is filled with yellow areas representing noise signals, blurring the distribution between subfields and aligning non-identical sub-fields, resulting in negative optimization, while the use of the DANN network can improve the cross-domain accuracy to a certain extent. With an improvement of 3% to 4% in various cross-domain tasks, the average accuracy rate is increased to 91.57%. This result indicates that domain-invariant features can be found through adversarial training. On the basis