The transverse magnetic field (TMF) contacts make the vacuum arcs deviate from the axisymmetric structure, so complete spatiotemporal evolution information of the plasma cannot be obtained by adopting one- or two-dimensional (2D) diagnostic methods. To address the issues, computer tomography was introduced in this paper. First, a multi-angle diagnostic imaging system based on split fiber bundles was proposed, which used a high-speed camera to simultaneously acquire eight angles of the arc image over time. In addition, a tomography algorithm called the maximum likelihood expectation maximum with Split Bregman denoising was proposed to reconstruct the dynamic spatiotemporal characteristics of the arc under complex conditions. Then, the three-dimensional (3D) distribution of Cu i and Cr i particles inside the contact gap was obtained by adopting optical filters. The 3D distribution of the vacuum arc had shown an obvious asymmetrical pattern under the TMF contacts, and there was a ring-like aggregation zone inside the arc, which can cause severe ablation on the anode contacts. According to the reconstructed 3D distribution of Cu i and Cr i, it is found that the metal vapor was mainly concentrated near the electrode surface and showed a clear distribution of non-uniform aggregates, while the concentration of particles in the gap was low. Moreover, on the cathode surface, the cathode spots moved in the form of groups driven by the TMF, while the anode surface was ablated by the electric arc, and the metal vapor existed in the form of bands.
Extensive attempts have been made to enable the application of deep learning to 3D plasma reconstruction. However, due to the limitation on the number of available training samples, deep learning-based methods have insufficient generalization ability compared to the traditional iterative methods. This paper proposes an improved algorithm named convolutional neural network-maximum likelihood expectation maximization-split-Bergman (CNN-MLEM-SB) based on the combination of the deep learning CNN and an iterative algorithm known as MLEM-SB. This method uses the prediction result of a CNN as the initial value and then corrects it using the MLEM-SB to obtain the final results. The proposed method is verified experimentally by reconstructing two types of vacuum arcs with and without transverse magnetic field (TMF) control. In addition, the CNN and the proposed algorithm are compared with respect to accuracy and generalization ability. The results show that the CNN can effectively reconstruct the arcs between a pair of disk contacts, which has specific distribution patterns: its structural similarity index measurement (SSIM) can reach 0.952. However, the SSIM decreases to 0.868 for the arc between a pair of TMF contacts, which is controlled by the TMF and has complex distribution patterns. Compared with the CNN reconstruction method, the proposed algorithm can achieve a higher reconstruction accuracy for any arc shape. Compared with the iterative algorithm, the proposed algorithm’s reconstruction efficiency is higher by 38.24% and 35.36% for the vacuum arc between the disk and the TMF contacts, respectively.
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