Anomaly Detection Techniques and Applications

Últimos artículos

• From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoring

  Nicolas Vautier, Paul Caron, Nardi Xhepi, F\'elicie Bizeul, Manel Boumghar, Christophe Degouy, Paul Boniol · 1 de octubre de 2026

  Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In industrial settings, such forecasts are generated daily to ensure supply-demand balance and optimal management of production assets. However, the incr…

• What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series

  Magali Parrino, Antoine Ajenjo, Emmanuel Remy, Pierre Stephan, Paul Boniol · 1 de octubre de 2026

  EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streaming method in unsupervised settings, we compare streaming methods with state-of-the-art TSAD models deployed online on a real nuclear power plant datase…

• In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams

  Magali Parrino, Antoine Ajenjo, Emmanuel Remy, Pierre Stephan, Pierre Senellart, Paul Boniol · 1 de octubre de 2026

  Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity. As a result, several works from the recent literature propose streaming anomaly detection methods that rely on incremental updates to adapt over time.…

• Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly Detection

  Ammar Bouketta, Smail Niar, Hamza Ouarnoughi, Eva Mutuzo Brindle · 1 de octubre de 2026

  Passenger access doors are safety-critical subsystems in railway vehicles, yet detecting abnormal door behavior in real operation is challenging because faults are rare, diverse, and often unlabeled. This paper addresses railway door condition monitoring as a cycle-level unsupervised anomaly detecti…

• TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization

  JinYoung Kim, Geonho Kim, GiJeong Park, Geonu Lee, YoungJoon Yoo · 1 de octubre de 2026

  CLIP is a powerful vision-language model, but it was not designed for fine-grained defect localization; CLIP-based anomaly detectors therefore adapt it with prompts or lightweight modules to increase defect sensitivity. We show that stronger sensitivity does not necessarily make local evidence relia…

• No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection

  Jiaheng Guo, Haochen Zhang, Yu-Chao Huang, Jinhao Duan, Nicholas Konz, Tianlong Chen · 30 de septiembre de 2026

  Time series anomaly detection (TSAD) plays a crucial role in healthcare, finance, industrial monitoring, and other sectors. Within and between these settings, anomalies span vastly different temporal scales, from sub-second point spikes to multi-hour drift patterns. However, most existing TSAD metho…

• Augmenting Visual Anomaly Detection with Automated Interpretability

  Antonio De Santis, Arsenio Leo, Marco Brambilla · 29 de septiembre de 2026

  Visual anomaly detectors identify deviations from known-normal data, but their anomaly signals may mix evidence of actual anomalies with benign visual variation. We investigate whether automated interpretability can augment visual anomaly detectors by identifying and intervening on different compone…

• When Less Compute Is More: Adaptive Early Exit Improves Pretrained Outlier Detection

  Tianyang Zhou, Leman Akoglu · 29 de septiembre de 2026

  Pretrained tabular foundation models process every dataset at a fixed depth, with inference costs growing with dataset size. To address this, we present the first study of depth-adaptive early-exit for pretrained outlier detection models. While early-exit is typically motivated by efficiency, we unc…

• VD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detection

  Mengyang Zhao, Zhuolin He, Haiyang Yu, Yuxuan Liang, Yifang Xu, Yuchuan Wu, Xiaolei Chen, Zhengtao Yao, Fan Shi, Yang Liu, Bin Li, Xiangyang Xue · 29 de septiembre de 2026

  Few-shot visual anomaly detection is fundamentally a visual comparison task, requiring fine-grained inspection of a query against normal references. Many recent methods based on large vision-language models (LVLMs) emphasize comparative reasoning through language chain-of-thought. Yet discrete, abst…

• Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing

  Yifei Gao, Tian Lan, Yimeng Lu, Xuming An, Meng Wang, Wenjun He, Yijie Li, Chen Zhang · 29 de septiembre de 2026

  Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing methods often rely on unsupervised objectives or surrogate abnormal patterns, providing limited supervision for context-depe…

• Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transport

  Haixiang Sun, Andrew L. Liu · 28 de septiembre de 2026

  Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ significantly from historical training data. In high-stakes applications, robustness often depends on rare cases that finite datasets fail to capture, makin…

• Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection

  Jianan Liu, Chunguang Li · 28 de septiembre de 2026

  Multi-dimensional time series, inherently tensorial, are common in practice. Despite great progress in time series anomaly detection, most existing methods are confined to uni-/multi-variate time series. When handling multi-dimensional time series using these methods, reshaping operations are requir…

• Adapting Visualization Techniques for Time-Series Anomaly Detection: From Convolutional Neural Networks to Convolutional-Recurrent Neural Networks

  Fabien Poirier, Myriam Lamolle · 28 de septiembre de 2026

  Deep neural networks achieve strong performance on complex tasks but are often regarded as "black boxes," which limits their adoption in domains where transparency is essential. This lack of interpretability raises ethical and legal concerns, particularly in sensitive applications such as security, …

• Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers

  Hyeongwon Kang, Jeongseob Kim, Jinwoo Park, Pilsung Kang · 28 de septiembre de 2026

  Time-series anomaly detection often returns scores or intervals, while analysts need to understand the abnormal behavior and the evidence supporting it. We introduce SAGE (Specialized Analyzer Group for Expert-like Detection), a multi-agent framework for evidence-grounded diagnosis of univariate tim…

• Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models

  Jordan Levy, Nicolas Verstaevel, Vincent Talon, Benoit Gaudou · 25 de septiembre de 2026

  Mobile robots require robust, real-time fault detection capable of continuous adaptation on constrained edge hardware. While deep time-series models excel at unsupervised anomaly detection, their computational cost prohibits high-frequency onboard execution. This paper bridges this gap via a Teacher…

• AIR: Analytic Imbalance Rectifier for Continual Learning

  Di Fang, Yinan Zhu, Zhiping Lin, Cen Chen, Ziqian Zeng, Huiping Zhuang · 25 de septiembre de 2026

  Continual learning (CL) agents incrementally learn from sequentially arriving data and adapt to the dynamic, ever-changing nature of real-world environments. However, many existing CL methods suffer performance degradation in evolving, imbalanced data streams due to limited adaptation to changing cl…

• Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space

  Jaron Yeh, Yen-Wei Chang, Jiang Liu, Shao-Yuan Lo · 25 de septiembre de 2026

  Industrial anomaly detection (IAD) is evolving beyond conventional detection and localization toward multimodal inspection systems that can describe, explain, and reason about fine-grained defects. Although recent multimodal large language model (MLLM)-based methods improve anomaly understanding thr…

• Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space

  Evangelia Zve, Gauvain Bourgne, Jean-Gabriel Ganascia · 25 de septiembre de 2026

  Some documents that embedding-based topic models initially classify as noise later become founding members of emerging topics. At publication time, however, they appear as scattered points in embedding space and are difficult to distinguish from ordinary noise without the benefit of hindsight. We st…

• Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data

  Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka · 25 de septiembre de 2026

  Semi-supervised anomaly detection, which aims to improve the anomaly detection performance by using a small amount of labeled anomaly data in addition to unlabeled data, has attracted attention. Existing semi-supervised approaches assume that most unlabeled data are normal, and train anomaly detecto…

• CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation

  Haochen Zhang, Jie Peng, Songyuan Sui, Yu-Chao Huang, Xiangqi Zhu, Tianlong Chen · 24 de septiembre de 2026

  Anomalous time series play a critical role in safety-critical domains, yet they are inherently scarce, heterogeneous, and costly to obtain. Existing time series generation methods predominantly focus on synthesizing normal data, providing limited value when anomalous samples are needed. We identify …

• Anomaly-Free Self-Optimization via AUC Bounds

  Kevin Wilkinghoff, Zheng-Hua Tan · 24 de septiembre de 2026

  Anomalies are rare, and anomalous data are often unavailable during development, making it difficult to determine which anomaly detection models and configurations will generalize to unseen anomalies. Recent approaches address this challenge by generating pseudo-anomalies and using bounds on the ach…

• Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?

  Kevin Wilkinghoff, Zheng-Hua Tan · 23 de septiembre de 2026

  Anomaly detection systems are often trained using normal data alone, while model selection and evaluation typically require labeled anomalies. We study whether anomaly detection performance can be predicted without access to anomalous data. For kNN-based detectors, we derive a lower bound on the are…

• Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus

  Diogo Risca, Afonso Louren\c{c}o, Ricardo Martins, Goreti Marreiros · 22 de septiembre de 2026

  We present a collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision loop. The system combines heterogeneous detectors and aggregates their outputs through a normalization-based weighted consensus, complemented by artif…

• Interpretable Multi-Hypersphere Deep Anomaly Detection for Open-set Supervised Anomaly Detection

  Zhiji Yang, Fangyong Wang, Yue Li, Xianli Pan, Jianhua Zhao · 22 de septiembre de 2026

  Multi-class open-set anomaly detection requires a model to characterize the normal acceptance domain formed by multiple heterogeneous subdistributions using only class-labeled samples from known normal classes, and to identify previously unseen anomalies at test time. Existing single-hypersphere met…

• Uncertainty-Weighted Fusion of Image and Synthetic Event for Video Anomaly Detection

  Sungheon Jeong, Jihong Park, Mohsen Imani · 22 de septiembre de 2026

  Most existing video anomaly detectors rely on RGB frames alone, which limit their ability to capture abrupt or transient motion cues that are critical for identifying anomalous events. We propose Uncertainty Weighted Image Event Fusion (IEF-VAD), a framework that integrates complementary RGB and syn…