SeaCausal-FL：面向海事物联网故障诊断与反事实推理的联邦模糊因果学习
SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoning
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中文总结 AI 辅助
SeaCausal-FL提出联邦模糊因果学习框架，结合区间二型模糊与结构因果模型，实现海事发动机故障诊断和反事实推理，在实验中取得高F1分数和稳健性能。
中文摘要 AI 辅助
海事物联网中可靠的船用发动机故障诊断面临分布式数据所有权、异构故障分布以及持续变化的运行条件等挑战。本文提出SeaCausal-FL，一种联邦模糊因果学习框架，将共享的时间诊断路径与机制条件因果推理相结合。区间二型模糊层表示不确定且重叠的运行机制，同时每个机制与一个物理约束的结构因果模型相关联。在聚合之前，本地学习的机制通过运行上下文、因果结构和条件干预-响应签名进行对齐。随后，模型参数根据样本、类别、机制和机制类别证据进行聚合，而非仅依据客户端样本量。学习到的结构方程进一步通过外推、行动和预测支持区间反事实推理。在船用发动机故障数据集和真实数据校准的半合成因果基准上的实验表明，SeaCausal-FL在四个客户端分区上的平均F1分数达到87.07%，AUROC和AUPRC分别为98.98%和94.81%。在训练期间未见负载和故障类型缺失的情况下，它也能保持强劲性能。在因果基准上，SeaCausal-FL达到约0.58的Edge-F1和0.68的Edge-AUPRC，将系数RMSE降低至约0.14，并提供有利的反事实估计和干预决策。
英文摘要
Reliable marine-engine fault diagnosis in maritime IoT is challenged by distributed data ownership, heterogeneous fault distributions, and continuously changing operating conditions. This paper proposes SeaCausal-FL, a federated fuzzy causal learning framework that combines a shared temporal diagnostic path with mechanism-conditioned causal reasoning. An interval type-2 fuzzy layer represents uncertain and overlapping operating mechanisms, while each mechanism is associated with a physics-constrained structural causal model. Before aggregation, locally learned mechanisms are aligned using operating context, causal structure, and conditional intervention-response signatures. Model parameters are then aggregated according to sample, class, mechanism, and mechanism-class evidence instead of client sample size alone. The learned structural equations further support interval counterfactual reasoning through abduction, action, and prediction. Experiments on a marine-engine fault dataset and a real-data-calibrated semi-synthetic causal benchmark show that SeaCausal-FL achieves an average F1 score of 87.07% across four client partitions, with AUROC and AUPRC of 98.98% and 94.81%, respectively. It also maintains strong performance under unseen loads and fault-type omission during training. On the causal benchmark, SeaCausal-FL reaches an Edge-F1 of approximately 0.58 and an Edge-AUPRC of 0.68, reduces coefficient RMSE to about 0.14, and provides favorable counterfactual estimation and intervention decisions.
发表机构
- Jimei University（集美大学）
- Hebei University of Technology（河北工业大学）
- R. M. K. Engineering College（R.M.K.工程学院）
- University of Science and Technology of China（中国科学技术大学）
- Waseda University（早稻田大学）
- University of Luxembourg（卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。
