## 4 Case Studies
### Case Study 1: Variable Operating Conditions Transfer Experiment  
Utilizing the sliding window truncation method, the raw vibration signal is processed into datasets under four different load scenarios (0, 1, 2, 3 HP) using the GAF. The dataset of each scenario contains seven types of faults, and each type of fault has 600 images. Thus, there are a total of 4200 images for operating conditions, and each dataset is randomly divided into the training dataset and testing dataset according to a ratio of 8:2. Detailed data descriptions of the dataset are presented in Table 2.  
Figure 8: The sliding window truncation method. Figure 9: Converted images in this work. In this case, the variable working conditions transfer experiment is mainly evaluated by the proposed method in these 12 tasks, i.e., T\({}_{01}\), T\({}_{02}\), T\({}_{03}\), T\({}_{10}\), T\({}_{12}\), T\({}_{13}\), T\({}_{20}\), T\({}_{21}\), T\({}_{23}\), T\({}_{30}\), T\({}_{31}\), T\({}_{32}\). For example, T\({}_{01}\) represents a variable operating condition transfer task with 0 HP load as the source domain and 1 HP load as the target domain. The rest of symbols T follow the same pattern as well. Part of the transfer details are shown in Table 3, where labeled proportion represents the proportion of labeled images in the training dataset. In this experiment, there are seven types of images, totaling 4200 images fed into the hybrid network, of which 3360 images were divided into training dataset and 840 images were divided into testing dataset. Then, half of the training data are randomly selected as labeled data and the remaining training data are treated as unlabeled data. The labeled data and unlabeled data are sent to the label generation model for training, and the artificial label prediction results of unlabeled data are output. Next, the labeled data and the artificially labeled data are imported together into the dynamic adversarial transfer network for training until the network converges. Finally, an accurate cross-domain fault diagnosis model is obtained. To illustrate effectiveness, the results of different transfer tasks are compared. In addition, average identification accuracy is selected as the model measurement standard; the accuracy is the average value of 10 rounds of experiments. Table 4 shows the comparison results. It can be seen from Table 3 that the proposed method can maintain an average accuracy of more than 99.17% in fault detection under diverse operating conditions, which indicates that the designed approach has powerful ability to solve the seven classifications problem with high efficiency. To sum up, this method can still maintain high accuracy of cross-domain label recognition under various working conditions, as well as in the absence of some source-domain labels, thus demonstrating the effectiveness of the fault transfer diagnosis network proposed in this article. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task Name** & **Motor Loads** & **Number of Labeled Data** & **Average Accuracy** \\ \hline T\({}_{01}\) & 0\(\rightarrow\)1 & 1680 & 99.88\% \\ T\({}_{02}\) & 0\(\rightarrow\)2 & 1680 & 99.26\% \\ T\({}_{03}\) & 0\(\rightarrow\)3 & 1680 & 99.38\% \\ T\({}_{10}\) & 1\(\rightarrow\)0 & 1680 & 98.25\% \\ T\({}_{12}\) & 1\(\rightarrow\)2 & 1680 & 99.28\% \\ T\({}_{13}\) & 1\(\rightarrow\)3 & 1680 & 98.71\% \\ T\({}_{20}\) & 2\(\rightarrow\)0 & 1680 & 98.62\% \\ T\({}_{21}\) & 2\(\rightarrow\)1 & 1680 & 99.71\% \\ T\({}_{23}\) & 2\(\rightarrow\)3 & 1680 & 99.64\% \\ T\({}_{30}\) & 3\(\rightarrow\)0 & 1680 & 98.15\% \\ T\({}_{31}\) & 3\(\rightarrow\)1 & 1680 & 99.42\% \\ T\({}_{32}\) & 3\(\rightarrow\)2 & 1680 & 99.72\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class Label** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Train/Test** \\ \hline Fault location & NO & BF & BF & IF & IF & OF & OF \\ Fault size (mil) & 0 & 7 & 14 & 7 & 14 & 7 & 14 \\ \hline \hline \end{tabular}
\end{table}