发表机构
Deakin University（迪肯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对工业系统的耦合故障检测问题，提出CMR-Mamba方法，通过因果正则化的Mamba编码器与kNN流形评分实现跨域故障检测，在多类耦合故障域上优于基准模型。
AI 中文摘要
工业系统中的无监督故障检测目前以基于重构的方法为主，这类方法监测单个传感器的边缘分布，却会忽略耦合故障——即传感器组间的物理关系发生断裂但边缘统计仍保持正常的故障。此类故障会规避边缘监测，作为潜在故障持续存在，直接影响系统的可靠性与安全性。我们提出CMR-Mamba（因果机制表示Mamba），该方法在健康数据上为每个域训练Mamba状态空间编码器。因果跨模态预测器对这些编码器进行正则化，使效应通道流形反映正常的因果耦合关系。异常通过该流形上的k近邻（kNN）距离，或观测值与因果预测效应嵌入间的机制残差进行评分。我们在机电（帕德博恩轴承）、液压（ZeMA）及网络物理（SWaT）耦合故障域对CMR-Mamba进行评估。 ablation 实验确立两项发现：其一，相较于编码器类型，kNN流形评分是较重构误差评分提升性能的主要来源，使基准模型的AUROC提升最多达0.42，且超过因果正则化带来的增益；其二，聚合AUROC被所有强方法均可解决的易故障饱和，因此各方法仅在低可分性子集上产生区分，在该子集上，CMR-Mamba在帕德博恩人工缺陷及SWaT隐秘攻击上优于所评估的基准模型——这类隐秘攻击使每个传感器均处于正常范围内，边缘方法仅能以随机概率检测到它们。因此，CMR-Mamba为机械、液压及网络物理系统的耦合故障检测提供了一种可解释且始终具有竞争力的方法。代码与数据可在该https URL获取。
英文摘要
Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal distributions. This misses coupling faults, where the physical relationship between sensor groups breaks while marginal statistics remain normal. Such faults evade marginal monitoring and persist as latent failures, with direct consequences for system reliability and safety. We propose CMR-Mamba (Causal Mechanism Representation Mamba), which trains per domain Mamba state-space encoders on healthy data. A causal cross-modal predictor regularises these encoders so that the effect-channel manifold reflects the normal cause-to-effect coupling. Anomalies are scored by k-nearest-neighbour (kNN) distance on this manifold or by the mechanism residual between the observed and the causally predicted effect embedding. We evaluate CMR-Mamba on electromechanical (Paderborn bearings), hydraulic (ZeMA) and cyber-physical (SWaT) coupling-fault domains. Ablations establish two findings. First, k-NN manifold scoring, rather than the encoder family, is the dominant source of gain over reconstruction-error scoring, improving baselines by up to 0.42 AUROC and exceeding the gain from causal regularisation. Second, aggregate AUROC is saturated by easy faults that any strong method solves, so the methods separate only on the low-separability subset. There CMR-Mamba leads the evaluated baselines on Paderborn artificial defects and on SWaT stealthy attacks, which keep every sensor inside its normal range and which marginal methods detect only at chance. CMR-Mamba therefore offers an interpretable and consistently competitive approach to coupling-fault detection across mechanical, hydraulic and cyber-physical systems. Code and data are available at https://anonymous.4open.science/status/CMR_Mamba_MFD_1177.
