mlsanityData to 7 Oct 2026

Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics

arXiv 2607.22987 ↗First posted 25 Jul 2026cs.LG cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Machinery fault detection (MFD) remains heavily reliant on supervised learning, which struggles with the scarcity of fault labels in real-world settings. While reinforcement learning (RL) offers a framework to model the sequential nature of degradation, current ``RL-based'' MFD methods reduce the problem to a static contextual bandit (CB) formulation: by ignoring state transitions and discarding the temporal discount factor, they collapse to standard supervised classification. We propose an adversarial inverse reinforcement learning (AIRL) framework that treats MFD as an offline IRL problem. Unlike reconstruction-based approaches that rely on static error margins, or CBs that ignore dynamics, our method recovers an intrinsic "health" reward directly from observational state transitions, requiring neither manual reward engineering nor fault labels. On three run-to-failure benchmarks (HUMS2023, IMS, XJTU-SY), AIRL is the only method achieving non-saturated post-detection consistency across all datasets, while CB baselines fail to detect gradual degradation and reconstruction models collapse into always-anomalous states. Code and data: https://github.com/dhirajneupane/AIRL-MFD-DN.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 12 of its 51 references that are in the map. First posted on arXiv 25 Jul 2026.

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12 of 51 references.

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  1. 2026Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection25 Feb

Other references · 11

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  2. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  3. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  4. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  5. 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
  6. 2018Generative Adversarial Imitation from Observationcited by 183
  7. 2021Off-Policy Imitation Learning from Observationscited by 70
  8. 2019Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Studycited by 17
  9. 2024Diffusion Imitation from Observationcited by 12
  10. 2024Data-driven Machinery Fault Diagnosis: A Comprehensive Reviewcited by 7
  11. 2021Finite sample guarantees for quantile estimation: An application to detector threshold tuningcited by 2

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  1. 2025Anomalous Decision Discovery using Inverse Reinforcement Learning
  2. 2026Failure Identification in Imitation Learning Via Statistical and Semantic Filtering
  3. 2020Sequential Anomaly Detection using Inverse Reinforcement Learning
  4. 2026Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring
  5. 2026Interactionless Inverse Reinforcement Learning: A Data-Centric Framework for Durable Alignment
  6. 2025Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policies
  7. 2021An Offline Deep Reinforcement Learning for Maintenance Decision-Making
  8. 2025AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning