Abstract：The operational lifetime of direct current (DC) circuit breakers is governed by cumulative degradation of the contact system caused by arc erosion, thermal stress, and morphological evolution during repeated interruption cycles. Accurate modeling and prediction of this degradation process are crucial for ensuring the long-term reliability and safe operation of DC switching equipment. This study establishes a physics-informed and data-driven hybrid framework for lifetime prediction of DC circuit breakers. The method integrates numerical simulation, experimental degradation measurement, and probabilistic regression modeling. A two-dimensional axisymmetric finite element model is developed to simulate the erosion process of the contact system under multiple breaking conditions. The model accounts for transient heat conduction, surface-tension-driven fluid flow (Marangoni effect), and phase transition of the contact material. Spatial- temporal distributions of temperature, velocity, and pressure are solved to characterize molten-pool evolution and quantify the mass loss of the contact material. The simulated mass loss, a function of arc current and arcing duration, serves as a structurally interpretable degradation indicator that compensates for the lack of direct measurement during service. Parallel experimental lifetime tests are performed on eight low-voltage DC circuit breakers rated at 500 V and 63 A, with test currents ranging from 63 A to 20 kA and time constants between 2 ms and 10 ms. During each breaking cycle, arc voltage and current are recorded to calculate the arc energy, while post-test measurements include contact resistance, surface roughness, and mass variation. The experiments reveal that contact degradation is accelerated by increasing current amplitude and prolonged arcing time, leading to dominant failure modes such as surface ignition, contact welding, and overtravel depletion. To effectively represent the complex degradation behavior, a two-stage nonlinear feature extraction strategy combining kernel principal component analysis (KPCA) and autoencoder (AE) is employed. KPCA first maps heterogeneous degradation data into a high-dimensional feature space to capture nonlinear correlations, followed by AE-based compression that encodes temporal degradation trajectories into compact latent representations. The resulting features preserve both statistical and physical information, reducing redundancy among electrical, thermal, and morphological parameters. A dual-channel Gaussian process regression (GPR) model is then constructed to perform lifetime prediction using dense-sampling features and sparse-sampling features. Independent regression channels are trained for each data type, and their predictions are fused through inverse-variance weighting to enhance stability under uncertain or incomplete information. Model validation shows that the proposed framework achieves a prediction error below 5% when full degradation information is available. In cases with partial or missing features, the hybrid model maintains acceptable accuracy and robustness. Although the simulation-derived mass-loss feature increases the prediction error from 5.4% to 18.1%, it is verified to identify early-stage degradation before pronounced morphological changes occur. The study establishes the nonlinear relationships between arc current, arcing time, and material erosion. The coupling between electrical and mechanical degradation mechanisms is elucidated. The hybrid modeling approach bridges the gap between physical interpretability and statistical generalization, providing a scalable route for predictive maintenance of electromechanical devices. Future work will focus on incorporating dynamic arc movement, material transfer effects, and multi-fault degradation pathways to extend the method toward real-time life assessment.
林靖怡, 武建文, 夏尚文, 陈儒盎. 基于双通道特征融合与高斯过程回归的直流断路器寿命预测方法[J]. 电工技术学报, 2026, 41(10): 3550-3562.
Lin Jingyi, Wu Jianwen, Xia Shangwen, Chen Ruang. Lifetime Prediction Method of DC Circuit Breakers Based on Dual-Channel Feature Fusion and Gaussian Process Regression. Transactions of China Electrotechnical Society, 2026, 41(10): 3550-3562.
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