## 3 Transformer Tank Cover Disassembly Algorithm
### Detection and Evaluation of Cover Types  
In transformer maintenance and recycling, accurately identifying the type and assessing the condition of the cover plate is crucial for automated disassembly. Transformer covers are primarily secured either by screws or welding. For screw-fastened covers, an inspection and evaluation of the screws is necessary, whereas welded covers necessitate specific cutting operations. Traditionally, operators determine disassembly strategies through manual inspection, which can be inefficient and pose safety risks. Therefore, implementing automated systems can enhance both the efficiency and safety of disassembly processes by accurately recognizing cover types and automatically assessing their conditions. Figure 5: Two types of transformer cover. (**a**) Screw-fastened cover. (**b**) Welded cover. In order to address the above problems, this paper proposes an automated identification scheme utilizing a pre-trained EfficientNetV2 [27] model. This model effectively classifies how the transformer cover is connected to the enclosure. \[C=\text{EfficientNetV2}(F_{\text{fusion}}). \tag{1}\]  
The system processes the fused feature map \(F_{\text{fusion}}\) to produce classification results, categorizing the connection method of the cover plate into screw-fastened or welded. This approach enables rapid and accurate identification, streamlining subsequent operations such as screw detection or welding and cutting tasks. For screw-fastened covers, the system necessitates the further detection and evaluation of screws, particularly assessing for deformation or slippage. This paper employs an improved Scan2 CAD [28] scheme, which accurately aligns RGB-D data with the transformer's CAD model, enhancing screw detection accuracy. Scan2 CAD extracts features from the RGB-D image \(I_{\text{RGB}}\) and the CAD model \(M_{\text{CAD}}\) using a 3D CNN, facilitating precise alignment. \[F_{\text{aligned}}=\text{Align}(F_{\text{RGBD}},F_{\text{CAD}};W_{\text{ CNN}}) \tag{2}\]  
where \(W_{\text{CNN}}\) is the weights of 3D CNN, and \(F_{\text{aligned}}\) is the aligned feature map. Based on the feature alignment, this paper utilizes the improved PointNet++ [29] model to detect screw and cover deformation. The system extracts the screw deformation features \(Z_{\text{screw}}\) and cover deformation features \(Z_{\text{plate}}\) using the PointNet++ model. \[Z_{\text{screw}}=f_{\text{PointNet++}}(F_{\text{aligned}}^{\text{ screw}}) \tag{3}\]  
\[Z_{\text{plate}}=f_{\text{PointNet++}}(F_{\text{aligned}}^{\text{Plate}}). \tag{4}\]  
The model analyzes the differences between the RGB-D data and the CAD model to assess severe deformation in screws and cover plates. To enhance its capability in complex scenarios, this paper introduces a historical data enhancement scheme. By incorporating historical maintenance data, the model adaptively adjusts the judgment threshold, optimizing detection for common failure modes such as screw slipping, thread wear, and local cover plate deformation. Historical data features are integrated into the PointNet++ model and compared with current inspection results to evaluate the similarity of failure modes. \[Z_{\text{history}}=f_{\text{history}}(H_{\text{history}}). \tag{5}\]  
The detection results of screw deformation are utilized not only to assess disassembly difficulty but also to estimate the torque required for unscrewing. To achieve this, this paper employs a graph neural network (GNN) to estimate the unscrewing moment \(T_{\text{estimate}}\)[30]. The GNN model leverages the geometric deformations of the screws and cover along with material properties, generating a force simulation based on the discrepancies between the RGB-D data and the CAD model, ultimately calculating the unscrewing moment.