# Transfer Learning Strategies for Deep Learning-based PHM Algorithms
## 4 Case, Transfer of Rotating Machinery Fault Diagnosis Algorithm
### Transfer of Fault Diagnosis Algorithm for Rotating Machinery under Different Working Conditions  
The vibration diagnosis data of the fault of the drive end bearing under 0 horsepower and 2 horsepower are used to transfer the fault diagnosis algorithm under different working conditions. The source domain is the drive end bearing of 0 horsepower, and the target domain is the drive end bearing of 2 horsepower.  
According to the ratio of the target domain of the source domain data, the data is divided into four groups. The target domain data size is 0.125 (this means the percent of used data and full data), 0.375, 0.625, and 0.875, respectively. We define a data set that is less than 50% of the total data as a small data set and a data set that is greater than 50% as a large data set. The impact of different target domain data size and transfer methods proposed in Section 3 on the transfer results was verified. The transfer process is shown in Figure 7. In TM1, the network weights are fully multiplexed from the source domain network and not need initializing. In TM2, the weights of the classification layer are initialized via glorot_uniform, and the remaining layers reuse the source domain network. In TM3, all network weights are via glorot_uniform.  
Figure 6: Training accuracy, training loss, test accuracy and test loss of CNN for bearing diagnosis.  
Figure 7: Flowchart of bearing fault diagnosis algorithm  
The test accuracy of the algorithm is a performance indicator when the algorithm is finally applied to actual tasks. Table 5 summarizes the test accuracy of the four groups of experiments. The experiments related to TM1 are performed only once, because the network parameters used by it are completely inherited from the source domain network, and the experimental results are fixed. The experiments related to TM2 and TM3 were repeated five times, and the results shown in the table are the average results of repeated experiments. The reason why no more repeated experiments were  
Figure 14: Comparison of three transfer methods under 0.875 data size.  
Figure 12: Comparison of three transfer methods under 0.375 data size.  
Figure 13: Comparison of three transfer methods under 0.625 data size.  
taken was that the results of the five experiments were almost the same, and the fluctuation was very small. Comparing the transfer methods, it can be seen that in both S1 and S2, the performance of TM1 and TM2 are higher than TM3, and the performance of TM2 is slightly higher than TM1. That is, as long as the source domain similarity of the target domain is high, as much as possible transfer the network structure and weights of the source domain and inheriting more source domain information can maximize the algorithm's fault diagnosis performance in the target domain. From the perspective of data size, it can be seen that under each transfer method, increasing the target domain data size improves the algorithm performance, but TM2's algorithm performance in 0.875 data size is almost equivalent to 0.125 data size. Using TM2 comes from TS1 for target domain fault diagnosis algorithm development can effectively save algorithm development costs and data acquisition costs in S1 or S2.