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This paper proposes a multimodal anomaly detection framework for fault detection in mechanical systems using self-supervised cross-modal reconstruction and adaptive thresholding to improve robustness under distribution shifts.
The paper presents AgentChaosBench, a benchmark for detecting and localizing runtime faults in LLM-based agentic systems, and evaluates it using zero-shot LLM baselines, revealing significant challenges in fault diagnosis.
The paper proposes CMR-Mamba, a causal mechanism monitoring method using Mamba state-space models for unsupervised industrial fault detection, focusing on coupling faults and evaluated on electromechanical, hydraulic, and cyber-physical systems.
This arXiv paper proposes collaborative control algorithms for federated multi-agent systems with AI agents and critics, applied to fault detection and cause analysis in network telemetry, with convergence guarantees via multi-time scale stochastic approximation.
This paper presents FDD-ON, a modular ontology for representing VAV HVAC system components, faults, symptoms, and impacts to enable interoperable fault detection and diagnostics applications.
The paper proposes an adversarial inverse reinforcement learning framework for machinery fault detection that learns a health reward from normal operational data without requiring fault labels, achieving consistent detection across multiple benchmarks.
This paper presents PASE, a neuro-symbolic framework that uses LLMs to generate structured recovery plans for cloud systems and verifies them via a neural-symbolic world model, achieving over 40% reduction in recovery time.
This paper introduces the Kalman Prototypical Network (KPN), a few-shot learning framework for fault detection in combined-cycle gas turbines. KPN models class prototypes as latent stochastic states to reduce variance and outperforms conventional methods on simulated leak detection tasks.
A benchmark study comparing traditional machine learning methods (Random Forest, XGBoost, SVM, Logistic Regression) against lightweight transformer variants (DistilBERT, TinyBERT, MobileBERT) for on-device fault detection across three public datasets. Traditional ML offers competitive accuracy at far smaller resource footprints, while TinyBERT-4L is the most deployment-friendly transformer.
This paper presents an offline anomaly detection framework using an LSTM autoencoder for Electro-Hydrostatic Actuators (EHAs), achieving an average accuracy of 99.0% and high recall on sensor data, outperforming classical methods.
TPA-AD is a two-stage pseudo anomaly-guided method for bearing time-series anomaly detection that generates pseudo-anomalous windows near normal boundaries using reconstruction models and contrastive learning, then scores anomalies with KNN—without requiring real anomaly samples during training. It is evaluated on bearing fault and degradation datasets, including high-speed train axle-box bearing data.
This paper introduces CAFD, a learning-based approach for DNN fault detection that integrates model-based, distance-based, and a novel concept-based feature called Concept Failure Ratio (CFR) derived from Vision-Language Models. CAFD consistently outperforms state-of-the-art baselines in fault detection rate across multiple datasets and budgets.