发表机构
LIX, École Polytechnique, Institut Polytechnique de Paris; Renault（巴黎综合理工学院LIX实验室，巴黎理工学院; 雷诺公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多模态工业机器人周期及故障数据稀缺的挑战，提出基于相空间重构的几何吸引子监测框架，用离散支持估计构建健康指标，在真实与合成数据集上性能优于深度学习基线。
AI 中文摘要
监测异构工业机器人集群的健康状况面临严峻挑战，其核心问题在于机器人运行周期的多模态特性，以及从运行到故障的相关数据持续稀缺。标准数据驱动方法，尤其是依赖序列重构的深度学习架构，在该特定场景中往往表现不佳；它们倾向于过度平滑复杂动态，掩盖早期退化迹象。为应对这些工业约束，我们通过基于相空间重构（Phase Space Reconstruction，PSR）的框架重新构建监测问题。该框架不预测时间序列，而是将单变量传感器数据转换为几何吸引子，明确展开机械状态，且与这些状态的时间发生无关。通过在该空间内评估各类异常评分技术，我们证明离散支持估计可提供一种有效且计算经济的健康指标（Health Indicator，HI）。我们在包含21台异构机器人、历时三年的真实数据集，以及合成朗之万系统上对该方法进行验证，结果显示其性能优于标准深度学习基线。我们表明，使算法偏差与目标系统的几何特性相匹配，可产生一种实用、可追溯且易于部署的方法，完美适配工业约束的实际情况。
英文摘要
Monitoring the health of heterogeneous industrial robot fleets is severely challenged by the multi-modal nature of their operational cycles and a persistent scarcity of run-to-failure data. Standard data-driven approaches, particularly deep learning architectures relying on sequential reconstruction, often struggle in this specific setting; they tend to over-smooth complex dynamics, masking early signs of degradation. To address these industrial constraints, we reframe the monitoring problem through a framework based on Phase Space Reconstruction (PSR). Instead of predicting temporal sequences, this framework transforms univariate sensor data into a geometric attractor, explicitly unfolding the mechanical states independently of their temporal occurrence. By evaluating various anomaly scoring techniques within this space, we demonstrate that discrete support estimation provides an effective and computationally frugal Health Indicator (HI). Validated on a real-world dataset of 21 heterogeneous robots over three years and a synthetic Langevin system, our approach outperforms standard deep learning baselines. We show that aligning the algorithmic bias with the geometric properties of the target system yields a pragmatic, traceable and easily deployable approach perfectly tailored to the realities of industrial constraints.
Comments17 pages, 6 figures. Accepted at ECML PKDD 2026
