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 8 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.

翻译：数字孪生技术日益广泛地应用于工业系统的监测与优化，然而现有众多框架仍存在难以解释、适应缓慢以及整合显式领域知识能力有限等问题。本文提出ANSR-DT，一种自适应神经符号框架，将时序异常检测、符号推理和基于强化学习的决策支持统一整合于单一数字孪生流水线中。ANSR-DT将用于多变量模式识别的CNN-LSTM模型与基于Prolog的推理相结合，后者能将学习到的信号转化为显式规则，从而实现透明的诊断与可追溯的决策路径。基于PPO的自适应层可在保持可解释性的前提下，进一步优化系统在动态工况下的运行响应。与8个基线模型的实验结果表明，ANSR-DT在提供具有竞争力的预测性能的同时，还能实现稳定的规则提取、可扩展的符号推理以及可操作的合理解释。在斯科尔科沃异常基准数据集（SKAB）上的额外验证进一步表明，该框架具备超越合成场景的泛化能力。这些发现将ANSR-DT定位为构建可信、自适应且可解释的工业数字孪生的实用基础框架。