将神经符号方法融合进数字孪生，实现可解释的自适应故障检测与决策。
ANSR-DT: A Neuro-Symbolic Framework for Adaptive and Explainable Digital Twins
- 用CNN-LSTM+Prolog实现多变量模式识别与规则生成
- 在SKAB数据集上表现优于8个基线，规则提取稳定可追踪
- 适合需要透明决策的工业系统监控场景
数字孪生被广泛用于监控和优化工业系统，但现有框架往往难以解释、适应缓慢，且难以融入显式领域知识。本文提出ANSR-DT，一种自适应神经符号框架，统一时间序列异常检测、符号推理与基于强化学习的决策支持。该框架结合CNN-LSTM模型进行多变量模式识别，并通过基于Prolog的推理将学习信号转化为显式规则，实现透明诊断与可追溯决策路径。一个基于PPO的自适应层在变化条件下持续优化操作响应，同时保持可解释性。与八个基线对比实验表明，ANSR-DT在预测性能上具有竞争力，同时具备稳定的规则提取、可扩展的符号推理和可操作的解释能力。在Skoltech异常基准（SKAB）上的额外验证进一步表明，该框架可泛化至非合成场景。这些结果使ANSR-DT成为可信、自适应、可解释工业数字孪生的实用基础。
原文摘要 · Abstract (English)
Digital twins are increasingly used to monitor and optimize industrial systems, yet many existing frameworks remain difficult to interpret, slow to adapt, and limited in their ability to incorporate explicit domain knowledge. This paper presents ANSR-DT, an adaptive neuro-symbolic framework that unifies temporal anomaly detection, symbolic reasoning, and reinforcement-learning-based decision support within a single digital twin pipeline. ANSR-DT combines a CNN-LSTM model for multivariate pattern recognition with Prolog-based reasoning that converts learned signals into explicit rules, enabling transparent diagnoses and traceable decision paths. A PPO-based adaptation layer further refines operational responses under changing conditions while preserving interpretability. Experiments against eight baselines show that ANSR-DT delivers competitive predictive performance together with stable rule extraction, scalable symbolic reasoning, and actionable explanations. Additional validation on the Skoltech Anomaly Benchmark (SKAB) further indicates that the framework transfers beyond synthetic settings. These findings position ANSR-DT as a practical foundation for trustworthy, adaptive, and explainable industrial digital twins.
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