Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring

[2608.14666] Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.14666 (cs) [Submitted on 1 Aug 2026] Title: Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring Authors: Dhiraj Neupane , Mohamed Reda Bouadjenek , Richard Dazeley , Sunil Aryal View a PDF of the paper titled Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring, by Dhiraj Neupane and 3 other authors View PDF HTML (experimental) Abstract: 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 this https URL . Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14666 [cs.AI] (or arXiv:2608.14666v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14666 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Dhiraj Neupane [ view email ] [v1] Sat, 1 Aug 2026 14:58:15 UTC (923 KB) Full-text links: Access Paper: View a PDF of the paper titled Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring, by Dhiraj Neupane and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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