用多智能体系统实现制造故障的实时因果诊断，可信且可解释。
CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing
- 基于神经符号架构的多智能体系统，统一异常检测与因果推理流程。
- 在真实工厂部署，端到端诊断延迟50-60秒，成功率高达98.73%。
- 模块化设计适合工业场景，支持人机协同与可扩展升级。
现代制造环境需要实时、可信且可解释的根因洞察以保障生产效率与质量。传统分析流程将异常检测、因果推断与根因分析割裂处理，限制了可扩展性与可解释性。本文提出CausalPulse，一个工业级多智能体协作诊断系统，用于智能制造中的因果诊断。该系统通过标准化代理协议构建神经符号架构，统一异常检测、因果发现与推理过程。CausalPulse已在罗伯特·博世工厂部署，无缝集成现有监控流程，支持生产规模下的实时运行。在公开数据集（Future Factories）和私有数据集（Planar Sensor Element）上的评估显示，整体成功率达98.0%和98.73%。按指标细分：规划与工具使用成功率98.75%，自我反思97.3%，协作99.2%。运行时实验表明，单次诊断全流程耗时50-60秒，具有近似线性扩展性（R²=0.97），验证其实时可用性。相较于现有工业协作者，CausalPulse在模块化、可扩展性与部署成熟度上具备显著优势。结果表明，其模块化、人机协同设计为下一代制造提供了可靠、可解释、可落地的自动化方案。
原文摘要 · Abstract (English)
Modern manufacturing environments demand real-time, trustworthy, and interpretable root-cause insights to sustain productivity and quality. Traditional analytics pipelines often treat anomaly detection, causal inference, and root-cause analysis as isolated stages, limiting scalability and explainability. In this work, we present CausalPulse, an industry-grade multi-agent copilot that automates causal diagnostics in smart manufacturing. It unifies anomaly detection, causal discovery, and reasoning through a neurosymbolic architecture built on standardized agentic protocols. CausalPulse is being deployed in a Robert Bosch manufacturing plant, integrating seamlessly with existing monitoring workflows and supporting real-time operation at production scale. Evaluations on both public (Future Factories) and proprietary (Planar Sensor Element) datasets show high reliability, achieving overall success rates of 98.0% and 98.73%. Per-criterion success rates reached 98.75% for planning and tool use, 97.3% for self-reflection, and 99.2% for collaboration. Runtime experiments report end-to-end latency of 50-60s per diagnostic workflow with near-linear scalability (R^2=0.97), confirming real-time readiness. Comparison with existing industrial copilots highlights distinct advantages in modularity, extensibility, and deployment maturity. These results demonstrate how CausalPulse's modular, human-in-the-loop design enables reliable, interpretable, and production-ready automation for next-generation manufacturing.
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