Modern vehicles generate thousands of different discrete events known as Diagnostic Trouble Codes (DTCs). Automotive manufacturers use Boolean combinations of these codes, called error patterns (EPs), to characterize system faults and ensure vehicle safety. Yet, EP rules are still manually handcrafted by domain experts, a process that is expensive and prone to errors as vehicle complexity grows. This paper introduces CAREP (Causal Automated Reasoning for Error Patterns), a multi-agent system that automatizes the generation of EP rules from high-dimensional event sequences of DTCs. CAREP combines a causal discovery agent that identifies potential DTC-EP relations, a contextual information agent that integrates metadata and descriptions, and an orchestrator agent that synthesizes candidate boolean rules together with interpretable reasoning traces. Evaluation on a large-scale automotive dataset with over 29,100 unique DTCs and 474 error patterns demonstrates that CAREP can automatically and accurately discover the unknown EP rules, outperforming LLM-only baselines while providing transparent causal explanations. By uniting practical causal discovery and agent-based reasoning, CAREP represents a step toward fully automated fault diagnostics, enabling scalable, interpretable, and cost-efficient vehicle maintenance.

翻译：现代车辆会产生数千种不同的离散事件，称为诊断故障码（DTC）。汽车制造商使用这些代码的布尔组合（称为错误模式（EP））来表征系统故障并确保车辆安全。然而，EP规则目前仍由领域专家手动构建，随着车辆复杂性的增加，这一过程不仅成本高昂且容易出错。本文介绍了CAREP（错误模式的因果自动推理），这是一个多智能体系统，能够从高维DTC事件序列中自动生成EP规则。CAREP结合了一个识别潜在DTC-EP关系的因果发现智能体、一个集成元数据和描述信息的上下文信息智能体，以及一个综合候选布尔规则并提供可解释推理轨迹的编排智能体。在一个包含超过29,100个独特DTC和474个错误模式的大规模汽车数据集上的评估表明，CAREP能够自动且准确地发现未知的EP规则，其性能优于仅使用LLM的基线方法，同时提供透明的因果解释。通过将实用的因果发现与基于智能体的推理相结合，CAREP代表了迈向全自动故障诊断的一步，实现了可扩展、可解释且经济高效的车辆维护。