Fault Detection

Ask about this topic

Overview

Fault detection studies how to recognize departures from normal operation and, increasingly, identify their source, severity, or likely consequences. Common approaches include statistical residuals and change-point tests, supervised classifiers, reconstruction-based anomaly detection, and temporal models trained on sensor, audio, image, or system-log data; physics-based models and domain knowledge can make their decisions easier to verify. Methods are tested on settings ranging from rotating machinery and power grids to industrial inspection, spacecraft telemetry, and software agents, with growing attention to online monitoring and edge deployment. A central challenge is detecting rare or unfamiliar faults while keeping false alarms low under changing operating conditions, and strong detection scores do not always translate into reliable fault localization or recovery.

Latest papers

CardsList

105 papers13 papers in the last four weeks, down 7% on the four weeks before. 0.1% of all new papers.

Jun 7, 2026

ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies

Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions. Existing online failure detectors either require white-box access to policy internals or add runtime overhead through resampling and observation-side signals. Our empirical analysis shows that emitted action chunks themselves already carry strong predictive signal for impending failures in generative robot policies. Motivated by this observation, we introduce ActProbe, a lightweight, pure action-space detector that uses two compact signals available from a single forward pass: Temporal Consistency Error (TCE) between consecutive action chunks and Action Chunk Magnitude (ACM) of the current chunk. ActProbe maps these signals to per-step failure probabilities with a task-conditioned LSTM-MLP architecture. Across a diverse suite of generative robot policies and benchmarks, ActProbe raises alerts before failures become visually recognizable, improving the accuracy (F1)-timeliness Pareto frontier of failure detection by an average hypervolume gain of +12.7% over both internal- and external-feature baselines, with a +9.0% early-detection ROC-AUC lead on unseen tasks. ActProbe further transfers to deployment, predicting failures on unseen real-robot pick tasks and accelerating RL fine-tuning (PPO) with 2.9x fewer environment interactions.

Bingjia Huang, Xiangyu Li, Xiang Wang +7

Institute for AI Industry Research (AIR), Tsinghua University · University of Electronic Science and Technology of China · Work done during internship at AIR, Tsinghua University. +1

Robot Policy LearningRobot Failure DetectionFault DetectionRobotic RLRobotic Anomaly Detection
AskPDF
Jun 2, 2026

TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection

This paper proposes a two-stage pseudo anomaly-guided anomaly detection method (Two-stage Pseudo Anomaly-guided Anomaly Detection, TPA-AD) for axle-box bearing time-series anomaly detection (time series anomaly detection, TSAD) under the setting where only normal samples are available for training. The method first generates pseudo-anomalous windows near the normal boundary using a reconstruction model and per-feature target-error control. It then learns anomaly-sensitive representations through contrastive learning between normal and pseudo-anomalous windows, and finally produces window-level and point-level anomaly scores using k-nearest neighbors (KNN). Compared with existing methods that rely on known fault categories, real anomaly priors, or random anomaly injection, TPA-AD improves the separability of the normal boundary by constructing pseudo-anomalies in boundary neighborhoods and can jointly handle continuous and discrete features in mixed-variable scenarios. The main experiments are conducted on bearing fault detection datasets and degradation-process datasets, with an additional exploratory extension on 1313 public TSAD datasets. The results show that the proposed method yields relatively stable anomaly responses, is sensitive to degradation evolution, and demonstrates a certain degree of broader applicability on public TSAD benchmarks and real high-speed-train-related bearing data.

Xiancheng Wang, Zhibo Zhang, Ran Li +4

School of Ocean Engineering, Harbin Institute of Technology, West Wenhua Road, Weihai, 264209, Shandong, China · Technical Center, Bogie Development Department, CRRC Qingdao Sifang Locomotive and Rolling Stock Co., Ltd, Jinhong East Road, Qingdao, 266111, Shandong, China · Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China

Fault DetectionUnsupervised Anomaly DetectionBearing Fault DiagnosisSynthetic Anomaly GenerationTime-Series Anomaly Detection
AskPDF
Jun 1, 2026

Monitoring Agentic Systems Before They're Reliable

Agentic systems entering production typically operate as partially integrated assemblies where structural defects, not task-level errors, dominate the failure landscape. At this maturity level, task-level error detection may be infeasible: structural failure modes mask the signal that task-level monitors are designed to detect.We present a monitoring and triage methodology that decomposes agentic system evaluation into three dimensions (quality, suitability, efficiency) at three monitoring scopes (within-run, cross-run, structural), using variance as a characterization signal. Findings are routed through severity classification adapted from FMEA, concentrating human attention on the subset that warrants investigation. We evaluate on a synthetic testbed of 220 runs across 120 document bundles with controlled error injection.Three results emerge. Monitor scope determines failure type: within-run monitors surface deterministic stage defects (CV = 0.02), cross-run monitors surface stochastic integration consequences (CV = 1.25, 24% at L2), and a structural monitor identifies an integration gap with perfect consistency (CV = 0.00). Injected task-level errors are indistinguishable from clean baselines, confirming structural defects mask task-level signal. Deterministic triage routes 97% of findings to automated tracking, leaving the 2% reflecting variable behavior for human investigation.We propose, on Stage 1 evidence, a maturity-staging model in which monitoring transitions from structural characterization to error detection to reliability tracking as integration defects resolve. The taxonomy, CV-based scope characterization, and severity model transfer architecturally to document-driven, multi-stage agentic workflows in regulated industries; specific calibrations are domain-specific. Deploy monitoring early: the first thing it finds is the most important thing to fix.

Marisa Ferrara Boston, Glen Hanson, Effi Georgala +2

Reins AI USA · Veraitech USA

Agent Failure AnalysisAgent ReliabilityAgentic WorkflowsFault DetectionAI Agent Monitoring
AskPDF
May 29, 2026

Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring

Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that compromise reliability in real-world deployment. Detecting such failures during execution is therefore critical for the robust deployment of embodied systems. Existing failure detection methods either rely on expensive action resampling or external models, while alternatives propagate trajectory-level labels uniformly across every timestep, obscuring localized failure signals. In this paper, we propose Hide-and-Seek, a framework that formulates VLA failure detection as a coarsely supervised learning problem. By combining inter-trajectory and intra-trajectory contrastive objectives, Hide-and-Seek localizes failure-indicative actions and induces temporally structured failure signals from trajectory-level supervision alone, without any step-level annotation. We evaluate Hide-and-Seek on LIBERO, VLABench, and a real-world robotic platform across three representative VLA policies: OpenVLA, π0π_0, and π0.5π_{0.5}.Our method achieves state-of-the-art multi-task failure detection performance with a practical accuracy--timeliness trade-off under conformal prediction, and generalizes well to both seen and unseen tasks.

Seongheon Park, Wendi Li, Changdae Oh +4

University of Wisconsin–Madison · Georgia Institute of Technology

Robot Failure DetectionFault DetectionWeakly Supervised LearningVision-Language-Action ModelsAnomaly DetectionFailure Detection for VLA Models
AskPDF
May 27, 2026

How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures

We discover that VLA architectures fail in fundamentally different, predictable ways at the motor-command level. Running VQ-BeT, Diffusion Policy, and ACT on identical evaluation protocols (n=450 episodes across PushT and ALOHA 14-DOF bimanual manipulation), we find: (1) direction reversal rate is a universal failure predictor across all three architectures (AUROC=0.93, 0.79, 0.91; p<0.001); (2) jerk monitoring is predictive only for discrete-token architectures, following a discrete-to-continuous gradient (0.88, 0.69, 0.41); (3) velocity violations alone are non-predictive everywhere (AUROC 0.41-0.69), yet velocity checking is the most common safety mechanism in VLA deployment code; and (4) for continuous-family VLAs, velocity monitoring provides effectively zero predictive signal (AUROC=0.52 on ACT, 0.41 on Diffusion), proving that architecture-matched monitor selection is essential. These results quantify a monitoring consequence of the well-known discrete/continuous VLA distinction: the two families produce qualitatively different failure signatures that require different monitors. No single monitor works universally; architecture-matched selection is required. This finding was enabled by SafeContract, a training-free, black-box action monitoring toolkit with conformal calibration. Code: https://github.com/krishnam94/vla-edge

Krishnam Gupta

Independent Research San Francisco, CA

Agent Failure AnalysisRobot Failure DetectionFault DetectionVision-Language-Action Models
AskPDF
May 26, 2026

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection

Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the first unsupervised anomaly detection framework incorporating a Diffusion Transformer. Raw test measurements are first compressed by an autoencoder, then reshaped into a structured token sequence enriched with sinusoidal and per-device wafer-position embeddings. Anomaly scores are derived from the noise-prediction error over mid-range diffusion timesteps, enabling fast wafer-scale screening without any labeled defects or manual feature engineering. Our approach achieves state-of-the-art performance on industrial 16nm IC test data under extreme class imbalance, offering interpretable failure localization through latent-space reconstruction residuals.

Yuxuan Yin, Chen He, Todd Jacobs +4

Department of Electrical and Computer Engineering, University of California Santa Barbara, CA, USA · Automotive Processing, NXP Semiconductors, TX, USA

Semiconductor ManufacturingIndustrial Anomaly DetectionFault DetectionAnomaly LocalizationUnsupervised Anomaly DetectionAnomaly Detection
AskPDF
May 25, 2026

A Universal Cliff and a Design Fingerprint: Cross-Section Defect Detection Under LLM Orchestration

Production language-model systems answer a request by partitioning it across an invisible orchestration of worker agents that recompose one integrated report. We ask what this does to a class of defect no single worker can see: a contradiction in the relation between two distant sections of a document. Holding the documents, defects, mechanism, scoring, and seed fixed, we vary only the model -- ten systems across five generations from one developer and five providers from distinct alignment paradigms. Two layers separate. First, a universal detection cliff: every model that finds these cross-section defects under a single agent loses that ability under orchestration, detection falling two-thirds or more across every paradigm tested. The cliff is mechanism-derived and not closed by scale or extended reasoning. Second, how models behave once fallen. A signal-detection decomposition shows that, among the six models discriminating above chance, only one developer's generations move along the reporting-criterion axis: as alignment is strengthened, the model misses fewer defects yet raises more false alarms on clean documents -- two faces of one criterion shift, scaling with generation within that developer (p < 0.001) and near-absent elsewhere. At the floor the missed defect is often not out of view: the model's private record reconstructs the structural fault accurately, while the integrated report signs off on its soundness, its concern spent on the artifact and an absent collaborator. This resists quantification -- an automated judge is unstable (precision 17-50%) and keywords cannot separate it from ordinary agreement -- a resistance we report as a finding. We release all runs, probes, defect keys, scorer prompts, and scripts. An integrated report's confidence is uninformative about partition-spanning defects, the most aligned systems are not the safest, and the cliff is structural.

Hiroki Fukui

Research Institute of Criminal Psychiatry · Department of Neuropsychiatry, Graduate School of Medicine, Kyoto University

Multi-Agent LLM SystemsLLM EvaluationFault DetectionLLM Agent Reliability
AskPDF
May 21, 2026

FAME: Failure-Aware Mixture-of-Experts for Message-Level Log Anomaly Detection

Production systems generate millions of log lines daily, yet most anomaly detectors operate at the session or window-level, flagging groups of lines rather than identifying the specific message responsible. This coarse granularity forces operators to inspect many routine lines per alert. Message-level detection offers finer granularity, but remains challenging. A single event template may correspond to both normal and anomalous messages, failures arise from heterogeneous subsystems, and line-level labeling at scale is impractical. Although large language models (LLMs) can reason over log semantics, applying them to every line is too costly for continuous monitoring. We present FAME (Failure-Aware Mixture-of-Experts), a label-efficient message-level mixture-of-experts framework that uses an LLM only once offline. We annotate at most K labeled lines per template to derive binary normal/anomaly indicators and representative examples. The LLM proposes a partition of templates into failure domains, and a certification step validates the proposal before training. FAME trains a lightweight router and domain experts that run on-premise and output anomaly predictions and failure-domain labels. On BGL, FAME achieves F1 = 98.16 at K = 100 reducing annotation effort by 76x and detects 86.3% of anomalies from unseen EventIDs. On Thunderbird, FAME reaches F1 = 99.95 with perfect recall.

Huanchi Wang, Zihang Huang, Yifang Tian +3

Department of Electrical and Computer Engineering University of Toronto, Toronto, Ontario

Fault DetectionMixture-of-Experts ModelsAnomaly Detection
AskPDF
May 7, 2026

PrefixGuard: Online Failure Warning and Trace-Grounded Diagnosis for LLM Agents

Large language model (LLM) agents execute multi-step tasks, but terminal verdicts arrive too late for intervention. Agent traces mix messages, tool calls, and feedback, making it difficult to learn failure signals from terminal outcomes. Even if a model learns to predict failure from execution prefixes, its predictions alone cannot show which actions, feedback, or unmet conditions need inspection. Existing work studies online failure prediction and diagnosis from completed trajectories, yet few methods connect timely warning to a compact symbolic model of execution behavior. We introduce PrefixGuard, a neuro-symbolic method that uses learned events to connect online failure warning with finite-state execution diagnosis. A gated recurrent unit (GRU) predicts horizon-specific failure risk from these events. A deterministic finite automaton (DFA) organizes their histories into compact risk-labeled paths for replay and rule-monitor composition. Across all 24 benchmark--horizon settings, PrefixGuard exceeds the strongest evaluated baseline in mean test area under the precision--recall curve (AUPRC). Average gains range from 12.0 to 21.9 percentage points. At a nominal 10% FAR budget, averaged test recall spans 56.1%--93.0% across benchmarks. On one frozen τ2τ^2-Bench DFA, model checking five specifications identifies violations in 21 of 265 benchmark-successful runs.

Xinmiao Huang, Jinwei Hu, Qisong He +4

University of Liverpool · Université Grenoble Alpes

Fault DetectionAI Agent MonitoringFinite-State MachineNeuro-Symbolic AILLM Agent ReliabilityFault Diagnosis
AskPDF
May 7, 2026

A Self-Healing Framework for Reliable LLM-Based Autonomous Agents

Autonomous agents based on Large Language Models (LLMs) are increasingly being utilized in complex software systems. However, reliability remains a significant challenge due to unpredictable failures such as hallucinations, execution errors, and inconsistent reasoning. This paper proposes a reliability-aware self-healing framework for LLM-based software agents. The framework integrates failure detection, reliability assessment, and automated recovery mechanisms. First, we define a taxonomy of failure types and introduce a quantitative reliability assessment model. Next, we propose a failure detection method that identifies abnormal agent behavior based on execution patterns and output consistency. Finally, we design a self-healing mechanism that dynamically recovers from failures through adaptive replanning and corrective prompting strategies. The proposed framework was implemented in a multi-agent workflow environment and evaluated using real-world task scenarios. Experimental results demonstrate that our approach significantly increases task success rates, reduces failure propagation, and enhances overall system robustness compared to existing methods. In particular, this study distinguishes itself by establishing an integrated monitoring system that combines the agent's internal reasoning process with external execution results. These findings are expected to contribute to securing the stability of advanced autonomous systems and lowering the barriers to LLM adoption in production environments.

Cheonsu Jeong, Younggun Shin

AX Center, SAMSUNG SDS, Seoul, South Korea · Dept. of Artificial Intelligence, Graduate School of Engineering, Yonsei University, Seoul, South Korea

Software Engineering AgentsFault DetectionLLM Agent Reliability
AskPDF
May 4, 2026

Distributed Observer-based Fault Detection over Intelligent Networked Multi-Vehicle Systems

Decentralized strategies are of interest for local decision-making over multi-vehicle networks. This paper studies mixed traffic networks of human-driven and autonomous vehicles with partial sensor measurements. The idea is to enable the group of connected autonomous vehicles (CAVs) to track the state of a group of human-driven vehicles (HDVs) via distributed consensus-based observers/estimators. Particularly, we make no assumption that the group of HDVs is locally observable in the direct neighborhood of any CAV. Then, the main contribution is to design local residual-based fault detection and isolation (FDI) at every CAV to detect possible faults/attacks in the sensor measurements. This distributed detection strategy enables every CAV to locally find possible anomalies in its taken sensor measurement with no need for a central processing unit. Two FDI logics are proposed with and without considering the history of the residuals. These FDI techniques are based on probabilistic threshold design on the residuals (in contrast to the existing deterministic threshold FDI techniques) with no assumption that the noise is of bounded support. This is more realistic in real-world multi-vehicle transportation systems.

Mohammadreza Doostmohammadian, Hamid R. Rabiee

Intelligent Transportation SystemsFault Detection
AskPDF
Apr 30, 2026

Hierarchical Fault Detection and Diagnosis for Transformer Architectures

Transformers now underpin critical AI systems across industry and research. Yet their faults can silently alter model behavior without runtime errors, and existing techniques offer little support for tracing these failures to their component and root cause. Such faults evade detection because loss and numerical values stay normal, and the visible symptom rarely identifies the component responsible. We present DEFault++, a hierarchical learning-based technique that first detects a fault, then identifies the affected component, and finally the cause within it, helping developers effectively debug transformer models. DEFault++ organizes component-level runtime measurements with a Fault Propagation Graph (FPG), a structural prior over the architecture's dependency paths, and reports the evidence behind each diagnosis. To train and evaluate it, we construct DEFault-bench, a benchmark of 5,556 labeled runs from mutation testing across seven models, nine tasks, and both encoder and decoder architectures. DEFault++ improves fault detection over four prior techniques, reaching an F1 of 0.826--0.909, and in a developer study with 21 participants, it raises repair accuracy from 57.1% to 83.3%. These results show that transformer fault diagnosis benefits from component-level measurements and architecture-aware reasoning rather than model-level behavior alone, and DEFault-bench provides a foundation for further research on transformer fault diagnosis.

Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma +1

Dalhousie University, Halifax, Canada

Software EngineeringAutomated Software TestingTransformerFault DetectionFault Diagnosis
AskPDF
Apr 23, 2026

Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark

In many cyber-physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is lacking due to data protection issues and partial observability. To combat this inherent lack of data, we introduce a high-fidelity, physics-informed co-simulation of a common aircraft main-fuel-pump system modelled in MATLAB/Simulink Simscape Fluids. We also describe its generated time-series data with health and fault mode annotations. To show feasibility of our benchmark, we apply an unsupervised Recurrent Variational Autoencoder (RNN-VAE) for anomaly detection and a SOM-VAE for operating mode discretization, trained to separate healthy and faulty conditions.

Felix Leonhard Janzen, Lukas Moddemann, Alexander Diedrich +1

Institute of Artificial Intelligence

Fault DetectionUnsupervised Anomaly DetectionTime-Series Anomaly DetectionPhysics-Based SimulationFault Diagnosis
AskPDF
Apr 17, 2026

Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks

Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal processing from physics enforcement: a three-layer leaky integrate-and-fire SNN estimates passive component parameters while a differentiable ODE solver provides physics-consistent training by decoupling the ODE physics loss from the unrolled spiking loop. On an EMI-corrupted synchronous buck converter benchmark, the SNN reduces lumped resistance error from 25.8%25.8\% to 10.2%10.2\% versus a feedforward baseline, within the ±10%\pm 10\% manufacturing tolerance of passive components, at a projected ∼270×{\sim}270\times energy reduction on neuromorphic hardware. Persistent membrane states further enable degradation tracking and event-driven fault detection via a +5.5+5.5 percentage-point spike-rate jump at abrupt faults. With 93%93\% spike sparsity, the architecture is suited for always-on deployment on Intel Loihi 2 or BrainChip Akida.

Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa +1

University of Wisconsin–Madison Madison, Wisconsin, USA · George Mason University Fairfax, Virginia, USA

Parameter EstimationPredictive MaintenanceFault DetectionSpiking Neural NetworksNeuromorphic ComputingPhysics-Informed ML
AskPDF
Apr 15, 2026

Adaptive Unknown Fault Detection and Few-Shot Continual Learning for Condition Monitoring in Ultrasonic Metal Welding

Ultrasonic metal welding (UMW) is widely used in industrial applications but is sensitive to tool wear, surface contamination, and material variability, which can lead to unexpected process faults and unsatisfactory weld quality. Conventional monitoring systems typically rely on supervised learning models that assume all fault types are known in advance, limiting their ability to handle previously unseen process faults. To address this challenge, this paper proposes an adaptive condition monitoring approach that enables unknown fault detection and few-shot continual learning for UMW. Unknown faults are detected by analyzing hidden-layer representations of a multilayer perceptron and leveraging a statistical thresholding strategy. Once detected, the samples from unknown fault types are incorporated into the existing model through a continual learning procedure that selectively updates only the final layers of the network, which enables the model to recognize new fault types while preserving knowledge of existing classes. To accelerate the labeling process, cosine similarity transformation combined with a clustering algorithm groups similar unknown samples, thereby reducing manual labeling effort. Experimental results using a multi-sensor UMW dataset demonstrate that the proposed method achieves 96% accuracy in detecting unseen fault conditions while maintaining reliable classification of known classes. After incorporating a new fault type using only five labeled samples, the updated model achieves 98% testing classification accuracy. These results demonstrate that the proposed approach enables adaptive monitoring with minimal retraining cost and time. The proposed approach provides a scalable solution for continual learning in condition monitoring where new process conditions may constantly emerge over time and is extensible to other manufacturing processes.

Ahmadreza Eslaminia, Kuan-Chieh Lu, Klara Nahrstedt +1

Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA · Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA · Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA

Fault DetectionClass-Incremental Learning
AskPDF
Apr 14, 2026

Transformer Based Machine Fault Detection From Audio Input

In recent years, Sound AI is being increasingly used to predict machine failures. By attaching a microphone to the machine of interest, one can get real time data on machine behavior from the field. Traditionally, Convolutional Neural Net (CNN) architectures have been used to analyze spectrogram images generated from the sounds captured and predict if the machine is functioning as expected. CNN architectures seem to work well empirically even though they have biases like locality and parameter-sharing which may not be completely relevant for spectrogram analysis. With the successful application of transformer-based models in the field of image processing starting with Vision Transformer (ViT) in 2020, there has been significant interest in leveraging these in the field of Sound AI. Since transformer-based architectures have significantly lower inductive biases, they are expected to perform better than CNNs at spectrogram analysis given enough data. This paper demonstrates the effectiveness of transformer-driven architectures in analyzing Sound data and compares the embeddings they generate with CNNs on the specific task of machine fault detection.

Kiran Voderhobli Holla

Fault DetectionTransformerAudio Classification
AskPDF
Apr 9, 2026

On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities

The increasing autonomy of spacecraft demands fault-detection systems that are both reliable and explainable. This work addresses eXplainable Artificial Intelligence for onboard Fault Detection, Isolation and Recovery within the Attitude and Orbit Control Subsystem by introducing a framework that enhances interpretability in neural anomaly detectors. We propose a method to derive low-dimensional, semantically annotated encodings from intermediate neural activations, called peepholes. Applied to a convolutional autoencoder, the framework produces interpretable indicators that enable the identification and localization of anomalies in reaction-wheel telemetry. Peepholes analysis further reveals bias detection and supports fault localization. The proposed framework enables the semantic characterization of detected anomalies while requiring only a marginal increase in computational resources, thus supporting its feasibility for on-board deployment.

Lorenzo Capelli, Leandro de Souza Rosa, Maurizio De Tommasi +8

University of Bologna, Italy · Thales Alenia Space Italia, Rome, Italy · European Space Agency (ESA-ESTEC), Netherlands

Interpretable Anomaly DetectionFault DetectionAnomaly DetectionNeural Network InterpretabilityFault Localization
AskPDF
Apr 7, 2026

A Weak-Signal-Aware Framework for Subsurface Defect Detection: Mechanisms for Enhancing Low-SCR Hyperbolic Signatures

Subsurface defect detection via Ground Penetrating Radar is challenged by "weak signals" faint diffraction hyperbolas with low signal-to-clutter ratios, high wavefield similarity, and geometric degradation. Existing lightweight detectors prioritize efficiency over sensitivity, failing to preserve low-frequency structures or decouple heterogeneous clutter. We propose WSA-Net, a framework designed to enhance faint signatures through physical-feature reconstruction. Moving beyond simple parameter reduction, WSA-Net integrates four mechanisms: Signal preservation using partial convolutions; Clutter suppression via heterogeneous grouping attention; Geometric reconstruction to sharpen hyperbolic arcs; Context anchoring to resolve semantic ambiguities. Evaluations on the RTSTdataset show WSA-Net achieves 0.6958 [email protected] and 164 FPS with only 2.412 M parameters. Results prove that signal-centric awareness in lightweight architectures effectively reduces false negatives in infrastructure inspection.

Wenbo Zhang, Zekun Long, Zican Liu +2

Road and Traffic Engineering Research Center, Zhejiang Normal University, Jinhua, Vhina · School of Information and Communication Technology, Griffith University, Brisbane, Australia · School of Cyber Science and Engineering, southeast university, Nanjing, China +1

Fault DetectionAnomaly DetectionRemote Sensing
AskPDF
Apr 5, 2026

Extended Hybrid Timed Petri Nets with Semi-Supervised Anomaly Detection for Switched Systems, Modelling and Fault Detection

Hybrid physical systems combine continuous and discrete dynamics, which can be simultaneously affected by faults. Conventional fault detection methods often treat these dynamics separately, limiting their ability to capture interacting fault patterns. This paper proposes a unified fault detection framework for hybrid dynamical systems by integrating an Extended Timed Continuous Petri Net (ETCPN) model with semi-supervised anomaly detection. The proposed ETCPN extends existing Petri net formalisms by introducing marking-dependent flow functions, enabling intrinsic coupling between discrete and continuous dynamics. Based on this structure, a mode-dependent hybrid observer is designed, whose stability under arbitrary switching is ensured via Linear Matrix Inequalities (LMIs), solved offline to determine observer gains. The observer generates residuals that reflect discrepancies between the estimated and measured outputs. These residuals are processed using semi-supervised methods, including One-Class SVM (OC-SVM), Support Vector Data Description (SVDD), and Elliptic Envelope (EE), trained exclusively on normal data to avoid reliance on labeled faults. The framework is validated through simulations involving discrete faults, continuous faults, and hybrid faults. Results demonstrate high detection accuracy, fast convergence, and robust performance, with OC-SVM and SVDD providing the best trade-off between detection rate and false alarms. The framework is computationally efficient for real-time deployment, as the main complexity is confined to the offline LMI design phase.

Fatiha Hamdi, Abdelhafid Zeroual, Fouzi Harrou

Department of Electronics, Faculty of technology, LAAAS Laboratory, Batna 2 University, Batna, Algeria. · Department of Electrical Engineering, Faculty of Science and Applied Sciences, Larbi Ben M’hidi University, Oum El Bouaghi, Algeria. · Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.

Semi-Supervised Anomaly DetectionFault DetectionAnomaly DetectionRegime-Switching Dynamical Systems
AskPDF
Apr 4, 2026

Shower-Aware Dual-Stream Voxel Networks for Structural Defect Detection in Cosmic-Ray Muon Tomography

We present SA-DSVN, a 3D convolutional architecture for voxel-level segmentation of structural defects in reinforced concrete using cosmic-ray muon tomography. Unlike conventional reconstruction methods (POCA, MLSD) that rely solely on muon scattering angles, our approach jointly processes scattering kinematics (9 channels) and secondary electromagnetic shower multiplicities (40 channels) through independent encoder streams fused via cross-attention. Training data were generated using Vega, a cloud-native Geant4 simulation framework, producing 4.5 million muon events across 900 volumes containing four defect types - honeycombing, shear fracture, corrosion voids, and delamination - embedded within a dense 7x7 rebar cage. A five-variant ablation study demonstrates that the shower multiplicity stream alone accounts for the majority of discriminative power, raising defect-mean Dice from 0.535 (scattering only) to 0.685 (shower only). On 60 independently simulated validation volumes, the model achieves 96.3% voxel accuracy, per-defect Dice scores of 0.59-0.81, and 100% volume-level detection sensitivity at 10 ms inference per volume. These results establish secondary shower multiplicity as a previously unexploited but highly effective feature for learned muon tomographic reconstruction.

Parthiv Dasgupta, Sambhav Agarwal, Palash Dutta +2

Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India

Fault DetectionComputed Tomography3D Semantic Segmentation
AskPDF
Apr 3, 2026

UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting

Although industrial inspection systems should be capable of recognizing unprecedented defects, most existing approaches operate under a closed-set assumption, which prevents them from detecting novel anomalies. While visual prompting offers a scalable alternative for industrial inspection, existing methods often suffer from prompt embedding collapse due to high intra-class variance and subtle inter-class differences. To resolve this, we propose UniSpector, which shifts the focus from naive prompt-to-region matching to the principled design of a semantically structured and transferable prompt topology. UniSpector employs the Spatial-Spectral Prompt Encoder to extract orientation-invariant, fine-grained representations; these serve as a solid basis for the Contrastive Prompt Encoder to explicitly regularize the prompt space into a semantically organized angular manifold. Additionally, Prompt-guided Query Selection generates adaptive object queries aligned with the prompt. We introduce Inspect Anything, the first benchmark for visual-prompt-based open-set defect localization, where UniSpector significantly outperforms baselines by at least 19.7% and 15.8% in AP50b and AP50m, respectively. These results show that our method enable a scalable, retraining-free inspection paradigm for continuously evolving industrial environments, while offering critical insights into the design of generic visual prompting.

Geonuk Kim, Minhoi Kim, Kangil Lee +5

LG Energy Solution

Industrial Anomaly DetectionIndustrial InspectionFault DetectionVisual Prompt LearningContrastive Learning
AskPDF
Apr 1, 2026

Exploring Silent Data Corruption as a Reliability Challenge in LLM Training

As Large Language Models (LLMs) scale in size and complexity, the consequences of failures during training become increasingly severe. A major challenge arises from Silent Data Corruption (SDC): hardware-induced faults that bypass system-level detection mechanisms. SDC may behave like benign numerical noise, but can also cause harmful gradient corruption that leads to loss spikes, divergence, or stalled progress. This work provides a controlled study of how intermittent SDC affects LLM pretraining. Using targeted fault injection at the level of GPU matrix-multiply instructions, we characterize the sensitivity of different bit positions, kernel functions, and execution stages. Our analysis shows that locally originating faults can produce impactful corruption, including NaN propagation, short-lived spikes in loss, gradient norm, and attention logits, as well as persistent parameter divergence. Building on the observed corruption signatures, we propose a lightweight detection method that identifies potentially harmful parameter updates. Experiments on LLaMA models with 60M, 350M, and 1.3B parameters demonstrate that recomputing the most recent training step upon detection can effectively mitigate the impact of these events.

Anton Altenbernd, Philipp Wiesner, Odej Kao

Technische Universität Berlin, Berlin, Germany

LLM ReliabilityLLM TrainingFault Detection
AskPDF
Mar 25, 2026

The impact of sensor placement on graph-neural-network-based leakage detection

Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we investigate how sensor placement influences the performance of GNN-based leakage detection. We propose a novel PageRank-Centrality-based sensor placement method and demonstrate that it substantially impacts reconstruction, prediction, and leakage detection on the EPANET Net1.

J. J. H. van Gemert, V. Breschi, D. R. Yntema +2

Control Systems Group, Dept. of Electrical Engineering, Eindhoven University of Technology, 5600MB Eindhoven, The Netherlands · Wetsus, Centre of Excellence for Sustainable Water Technology, 8900MA Leeuwarden, The Netherlands. · Mathematical and Statistical Methods – Biometris, Wageningen University, 6708PB Wageningen, the Netherlands

Fault DetectionGraph Neural Networks
AskPDF
Mar 24, 2026

From Prediction to Diagnosis: Reasoning-Aware AI for Photovoltaic Defect Inspection

Reliable photovoltaic defect identification is essential for maintaining energy yield, ensuring warranty compliance, and enabling scalable inspection of rapidly expanding solar fleets. Although recent advances in computer vision have improved automated defect detection, most existing systems operate as opaque classifiers that provide limited diagnostic insight for high-stakes energy infrastructure. Here we introduce REVL-PV, a vision-language framework that embeds domain-specific diagnostic reasoning into multimodal learning across electroluminescence, thermal, and visible-light imagery. By requiring the model to link visual evidence to plausible defect mechanisms before classification, the framework produces structured diagnostic reports aligned with professional photovoltaic inspection practice. Evaluated on 1,927 real-world modules spanning eight defect categories, REVL-PV achieves 93% classification accuracy while producing interpretable diagnostic rationales and maintaining strong robustness under realistic image corruptions. A blind concordance study with a certified solar inspection expert shows strong semantic alignment between model explanations and expert assessments across defect identification, root-cause attribution, and visual descriptions. These results demonstrate that reasoning-aware multimodal learning establishes a general paradigm for trustworthy AI-assisted inspection of photovoltaic energy infrastructure.

Dev Mistry, Feng Qiu, Bo Chen +4

Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA · Argonne National Laboratory, Lemont, IL 60439, USA · Smart Grid Emerging Technology Department, Commonwealth Edison, Chicago, IL 60618, USA +2

Fault DetectionIndustrial InspectionMultimodal ReasoningMultimodal Model Interpretability
AskPDF
Mar 20, 2026

Can Large Multimodal Models Inspect Buildings? A Hierarchical Benchmark for Structural Pathology Reasoning

Automated building facade inspection is a critical component of urban resilience and smart city maintenance. Traditionally, this field has relied on specialized discriminative models (e.g., YOLO, Mask R-CNN) that excel at pixel-level localization but are constrained to passive perception and worse generization without the visual understandng to interpret structural topology. Large Multimodal Models (LMMs) promise a paradigm shift toward active reasoning, yet their application in such high-stakes engineering domains lacks rigorous evaluation standards. To bridge this gap, we introduce a human-in-the-loop semi-automated annotation framework, leveraging expert-proposal verification to unify 12 fragmented datasets into a standardized, hierarchical ontology. Building on this foundation, we present DefectBench, the first multi-dimensional benchmark designed to interrogate LMMs beyond basic semantic recognition. DefectBench evaluates 18 state-of-the-art (SOTA) LMMs across three escalating cognitive dimensions: Semantic Perception, Spatial Localization, and Generative Geometry Segmentation. Extensive experiments reveal that while current LMMs demonstrate exceptional topological awareness and semantic understanding (effectively diagnosing "what" and "how"), they exhibit significant deficiencies in metric localization precision ("where"). Crucially, however, we validate the viability of zero-shot generative segmentation, showing that general-purpose foundation models can rival specialized supervised networks without domain-specific training. This work provides both a rigorous benchmarking standard and a high-quality open-source database, establishing a new baseline for the advancement of autonomous AI agents in civil engineering.

Hui Zhong, Yichun Gao, Luyan Liu +4

Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Hong Kong University of Science and Technology, Hongkong, China · Hong Kong University of Science and Technology, Hong Kong, China +1

Zero-Shot Referring Image SegmentationMultimodal Model EvaluationFault DetectionSemantic Segmentation
AskPDF
Mar 19, 2026

HEP Statistical Inference for UAV Fault Detection: CLs, LRT, and SBI Applied to Blade Damage

This paper transfers three statistical methods from particle physics to multirotor propeller fault detection: the likelihood ratio test (LRT) for binary detection, the CLs modified frequentist method for false alarm rate control, and sequential neural posterior estimation (SNPE) for quantitative fault characterization. Operating on spectral features tied to rotor harmonic physics, the system returns three outputs: binary detection, controlled false alarm rates, and calibrated posteriors over fault severity and motor location. On UAV-FD, a hexarotor dataset of 18 real flights with 5% and 10% blade damage, leave-one-flight-out cross-validation gives AUC 0.862 +/- 0.007 (95% CI: 0.849--0.876), outperforming CUSUM (0.708 +/- 0.010), autoencoder (0.753 +/- 0.009), and LSTM autoencoder (0.551). At 5% false alarm rate the system detects 93% of significant and 81% of subtle blade damage. On PADRE, a quadrotor platform, AUC reaches 0.986 after refitting only the generative models. SNPE gives a full posterior over fault severity (90% credible interval coverage 92--100%, MAE 0.012), so the output includes uncertainty rather than just a point estimate or fault flag. Per-flight sequential detection achieves 100% fault detection with 94% overall accuracy.

Khushiyant

Department of Computer Science, University of Freiburg

Likelihood Ratio TestingFault DetectionFault DiagnosisUAV DetectionStatistical Hypothesis Testing
AskPDF
Mar 18, 2026

MAED: Mathematical Activation Error Detection for Mitigating Physical Fault Attacks in DNN Inference

The inference phase of deep neural networks (DNNs) in embedded systems is increasingly vulnerable to fault attacks and failures, which can result in incorrect predictions. These vulnerabilities can potentially lead to catastrophic consequences, making the development of effective mitigation techniques essential. In this paper, we introduce MAED (Mathematical Activation Error Detection), an algorithm-level error detection framework that exploits mathematical identities to continuously validate the correctness of non-linear activation function computations at runtime. To the best of our knowledge, this work is the first to integrate algorithm-level error detection techniques to defend against both malicious fault injection attacks and naturally occurring faults in critical DNN components in embedded systems. The evaluation is conducted on three widely adopted activation functions, namely ReLu, sigmoid, and tanh which serve as fundamental building blocks for introducing non-linearity in DNNs and can lead to mispredictions when subjected to natural faults or fault attacks. We assessed the proposed error detection scheme via fault model simulation, achieving close to 100% error detection while mitigating existing fault attacks on DNN inference. Additionally, the overhead introduced by integrating the proposed scheme with the baseline implementation (i.e., without error detection) is validated through implementations on an AMD/Xilinx Artix-7 FPGA and an ATmega328P microcontroller, as well as through integration with TensorFlow. On the microcontroller, the proposed error detection incurs less than 1% clock cycle overhead, while on the FPGA it requires nearly zero additional area, at the cost of approximately a 20% increase in latency for sigmoid and tanh.

Kasra Ahmadi, Saeed Aghapour, Mehran Mozaffari Kermani +1

Department of Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA. · Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA.

Fault DetectionEdge Inference
AskPDF
Mar 17, 2026

When GPUs Fail Quietly: Observability-Aware Early Warning Beyond Numeric Telemetry

GPU nodes are central to modern HPC and AI workloads, yet many failures do not manifest as immediate hard faults. While some instabilities emerge gradually as weak thermal or efficiency drift, a significant class occurs abruptly with little or no numeric precursor. In these detachment-class failures, GPUs become unavailable at the driver or interconnect level and the dominant observable signal is structural, including disappearance of device metrics and degradation of monitoring payload integrity. This paper proposes an observability-aware early-warning framework that jointly models (i) utilization-aware thermal drift signatures in GPU telemetry and (ii) monitoring-pipeline degradation indicators such as scrape latency increase, sample loss, time-series gaps, and device-metric disappearance. The framework is evaluated on production telemetry from GPU nodes at GWDG, where GPU, node, monitoring, and scheduler signals can be correlated. Results show that detachment failures exhibit minimal numeric precursor and are primarily observable through structural telemetry collapse, while joint modeling increases early-warning lead time compared to GPU-only detection. The dataset used in this study is publicly available at https://doi.org/10.5281/zenodo.19052367.

Michael Bidollahkhani, Freja Nordsiek, Julian M. Kunkel

Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG) Georg-August-Universität Göttingen Göttingen, Germany

Fault DetectionPredictive MaintenanceTime-Series Anomaly DetectionHigh-Performance Computing
AskPDF
Mar 16, 2026

A Self-Evolving Defect Detection Framework for Industrial Photovoltaic Systems

Reliable photovoltaic (PV) power generation requires timely detection of module defects that may reduce energy yield, accelerate degradation, and increase lifecycle operation and maintenance costs during field operation. Electroluminescence (EL) imaging has therefore been widely adopted for PV module inspection. However, automated defect detection in real operational environments remains challenging due to heterogeneous module geometries, low-resolution imaging conditions, subtle defect morphology, long-tailed defect distributions, and continual data shifts introduced by evolving inspection and labeling processes. These factors significantly limit the robustness and long-term maintainability of conventional deep-learning inspection pipelines. To address these challenges, this paper proposes SEPDD, a Self-Evolving Photovoltaic Defect Detection framework designed for evolving industrial PV inspection scenarios. SEPDD integrates automated model optimization with a continual self-evolving learning mechanism, enabling the inspection system to progressively adapt to distribution shifts and newly emerging defect patterns during long-term deployment. Experiments conducted on both a public PV defect benchmark and a private industrial EL dataset demonstrate the effectiveness of the proposed framework. Both datasets exhibit severe class imbalance and significant domain shift. SEPDD achieves a leading mAP50 of 91.4% on the public dataset and 49.5% on the private dataset. It surpasses the autonomous baseline by 14.8% and human experts by 4.7% on the public dataset, and by 4.9% and 2.5%, respectively, on the private dataset.

Haoyu He, Yu Duan, Wenzhen Liu +5

EcoFlow Inc, Shenzhen, China · School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China

Fault DetectionIndustrial InspectionContinual LearningDomain Adaptation
AskPDF
Mar 16, 2026

A Lightweight, Transferable, and Self-Adaptive Framework for Intelligent DC Arc-Fault Detection in Photovoltaic Systems

Arc-fault circuit interrupters (AFCIs) are essential for mitigating fire hazards in residential photovoltaic (PV) systems, yet achieving reliable DC arc-fault detection under real-world conditions remains challenging. Spectral interference from inverter switching, hardware heterogeneity, operating-condition drift, and environmental noise collectively compromise conventional AFCI solutions. This paper proposes a lightweight, transferable, and self-adaptive learning-driven framework (LD-framework) for intelligent DC arc-fault detection. At the device level, LD-Spec learns compact spectral representations enabling efficient on-device inference and near-perfect arc discrimination. Across heterogeneous inverter platforms, LD-Align performs cross-hardware representation alignment to ensure robust detection despite hardware-induced distribution shifts. To address long-term evolution, LD-Adapt introduces a cloud-edge collaborative self-adaptive updating mechanism that detects unseen operating regimes and performs controlled model evolution. Extensive experiments involving over 53,000 labeled samples demonstrate near-perfect detection, achieving 0.9999 accuracy and 0.9996 F1-score. Across diverse nuisance-trip-prone conditions, including inverter start-up, grid transitions, load switching, and harmonic disturbances, the method achieves a 0% false-trip rate. Cross-hardware transfer shows reliable adaptation using only 0.5%-1% labeled target data while preserving source performance. Field adaptation experiments demonstrate recovery of detection precision from 21% to 95% under previously unseen conditions. These results indicate that the LD-framework enables a scalable, deployment-oriented AFCI solution maintaining highly reliable detection across heterogeneous devices and long-term operation.

Xiaoke Yang, Long Gao, Haoyu He +5

EcoFlow Inc, Shenzhen, China. · AAU Energy, Aalborg University, Denmark

Fault DetectionPower SystemsDomain AdaptationTransfer LearningContinual Learning
AskPDF