mlsanityData to 14 Oct 2026

Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring

arXiv 2608.14666 ↗First posted 1 Aug 2026cs.AIcited by 0

On the map: State-space sequence models (S4, Mamba) → Time series forecasting with SSMs

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Time series forecasting with SSMs, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 14 of its 50 references that are in the map. First posted on arXiv 1 Aug 2026.

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14 of 50 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2013Auto-Encoding Variational Bayescited by 4,053
  4. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  5. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,465
  6. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  7. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  8. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  9. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  10. 2024Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedycited by 13
  11. 2025Autoencoders for Anomaly Detection are Unreliablecited by 9
  12. 2024Data-driven Machinery Fault Diagnosis: A Comprehensive Reviewcited by 7
2 more
  1. 2026Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detectioncited by 2
  2. 2024Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoringcited by 1

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