A Multi-Agent System for Automated Lifetime Prediction of Power Electronics

Abstract

Accurate lifetime prediction of power semiconductor devices is critical for the reliability of power electronic systems. Power cycling tests generate only a small number of failure samples due to their high cost and long duration. This data scarcity makes it difficult to train reliable prediction models. At the same time, modern deep learning models require significant AI expertise to configure and tune, which creates a barrier for reliability engineers. To address these two problems, this paper proposes a domain-constrained multi-agent framework for automated remaining useful life (RUL) prediction of power semiconductor devices. The framework integrates three collaborative agents: a Stats Agent for statistical reliability assessment, a Physics Agent for physical plausibility validation, and a Model Agent for algorithm selection and hyperparameter tuning. A Central Controller manages the workflow and coordinates agent communication. The agents interact through a Propose-Critique-Refine (PCR) mechanism, in which the Model Agent proposes configurations and the other two agents critique them from statistical and physical perspectives. This iterative negotiation eliminates the need for manual parameter tuning. The framework is validated on the NASA IGBT accelerated aging dataset under Leave-One-Device-Out Cross-Validation. Results show that the framework achieves an R2 of 0.914 at a 50% observation ratio and remains above 0.79 even at 30%, demonstrating strong generalization under limited data conditions.