Leak detection models for multiphase pipelines often degrade when deployed under flow regimes that differ from training. Existing evaluations typically assess performance under in-distribution operating conditions, masking failures caused by regime transitions such as bubble-to-slug flow.
arXiv:2608. 04805v1 Announce Type: new Abstract: Leak detection models for multiphase pipelines often degrade when deployed under flow regimes that differ from training.
Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particularly attractive due to their efficiency, as they operate directly on pre-trained networks without requiring retraining.
arXiv:2606. 29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment.
CF-YOLO introduces a real‑time detection framework for camouflaged micro‑defects on industrial components, combining a Context‑Perception Aggregation Module (CPAM) that fuses large‑kernel macro‑texture cues with small‑kernel boundary details, and a Feature Additive Refinement Module (FARM) that globally refines fine‑grained anomaly representations. The authors also release the Copper Tube Defect Dataset (CTDD), a benchmark of 1,847 images with 4,898 annotated defect boxes. Experiments show CF‑YOLO outperforms baseline detectors such as YOLOv11 by 2.2% in mAP@50 and 3.9% in Precision while preserving real‑time speed.
arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.
arXiv:2604. 26633v2 Announce Type: replace-cross Abstract: Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is slow and costly.
The paper "Mind the Gap: Robustness Risks in PII Detection Systems" examines how modern PII detection methods—encoder-based NER, rule-based hybrid detection, and generative LLM extraction—fail when faced with realistic distribution shifts such as noisy, unstructured, and informal inputs. A stress‑test benchmark covering seven categories of natural shift reveals that each architecture degrades in distinct ways: encoder models struggle with unseen surface forms and boundary detection, rule‑based systems falter on non‑standard formats, and LLMs suffer from entity‑type confusion and generation instability. The authors argue that aggregate benchmark scores hide these deployment‑critical weaknesses and propose a hybrid detection pipeline with a QA‑driven feedback loop to iteratively mitigate risks, also releasing the benchmark for OOD‑aware evaluation.
The paper introduces a leakage‑free 3×3 spatio‑temporal partition for evaluating inductive kriging, ensuring training, validation, and testing occur on distinct spatial and temporal domains. It proposes DRIK, a framework that includes Spatial Continuity Regularization, Masked Flow Disambiguation, and Structural Domain Expansion to mitigate structural shifts from unseen nodes. Experiments on six datasets show DRIK outperforms existing baselines, reducing MAE by up to 12.48% and achieving lower test‑to‑validation MAE ratios under the stricter evaluation protocol.
arXiv:2606. 29339v1 Announce Type: cross Abstract: Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring.
arXiv:2610.27891v1 Announce Type: cross Abstract: Industrial fluid transport systems are monitored through heterogeneous sensing streams, but robust fault diagnosis remains difficult because response...
Reliable event detection underpins induced-seismicity monitoring for Carbon dioxide Capture and Storage (CCS) and geothermal operations, distributed acoustic sensing (DAS), and industrial condition monitoring. In each setting a detector must stay reliable both when sensors fail and when the signal is buried in noise.
arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.