{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/fault-detection/papers/2","list_of":"/task/fault-detection","task":"Fault Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":6,"rows_per_page":100,"rows":[101,200],"of":511,"counts":{"archive_papers_tagged":511,"with_a_code_link":80,"where_syntology_ran_a_sample":2,"not_listed_spam_title":0,"listed":511,"listed_where_code_ran":2,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":2,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/fault-detection","prev":"/task/fault-detection","next":"/task/fault-detection/papers/3","papers":[{"url":null,"slug":"fault-detection-and-human-intervention-in","title":"Fault Detection and Human Intervention in Vehicle Platooning: A Multi-Model Framework","date":"2025-04-28","arxiv_id":"2504.20209","repositories_listed":0,"syntology":null},{"url":null,"slug":"epsilon-adaptive-fault-mitigation-in","title":"EPSILON: Adaptive Fault Mitigation in Approximate Deep Neural Network using Statistical Signatures","date":"2025-04-24","arxiv_id":"2504.20074","repositories_listed":0,"syntology":null},{"url":null,"slug":"dconad-a-differencing-based-contrastive","title":"DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection","date":"2025-04-19","arxiv_id":"2504.14204","repositories_listed":0,"syntology":null},{"url":null,"slug":"equi-euler-graphnet-an-equivariant-temporal","title":"Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems","date":"2025-04-18","arxiv_id":"2504.13768","repositories_listed":0,"syntology":null},{"url":null,"slug":"correcting-domain-shifts-in-electric-motor","title":"Correcting Domain Shifts in Electric Motor Vibration Data for Unseen Operating Conditions","date":"2025-04-14","arxiv_id":"2504.10661","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-steady-state-compliance-testing-of","title":"Modelling & Steady State Compliance Testing of an Improved Time Synchronized Phasor Measurement Unit Based on IEEE Standard C37.118.1","date":"2025-04-14","arxiv_id":"2504.09883","repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-input-and-state-estimation-under","title":"Simultaneous Input and State Estimation under Output Quantization: A Gaussian Mixture approach","date":"2025-04-13","arxiv_id":"2504.09711","repositories_listed":0,"syntology":null},{"url":null,"slug":"naper-fault-protection-for-real-time-resource","title":"NAPER: Fault Protection for Real-Time Resource-Constrained Deep Neural Networks","date":"2025-04-09","arxiv_id":"2504.06591","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-method-for-fault-detection-and","title":"A Robust Method for Fault Detection and Severity Estimation in Mechanical Vibration Data","date":"2025-04-04","arxiv_id":"2504.03229","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-injection-analysis-of-real-nvp","title":"Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection","date":"2025-04-02","arxiv_id":"2504.02015","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-fault-detection-and-classification-of","title":"Online Fault Detection and Classification of Chemical Process Systems Leveraging Statistical Process Control and Riemannian Geometric Analysis","date":"2025-04-02","arxiv_id":"2504.01276","repositories_listed":0,"syntology":null},{"url":null,"slug":"unifault-a-fault-diagnosis-foundation-model","title":"UniFault: A Fault Diagnosis Foundation Model from Bearing Data","date":"2025-04-02","arxiv_id":"2504.01373","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-yolo-based-semi-automated-labeling-approach","title":"A YOLO-Based Semi-Automated Labeling Approach to Improve Fault Detection Efficiency in Railroad Videos","date":"2025-04-01","arxiv_id":"2504.01010","repositories_listed":0,"syntology":null},{"url":null,"slug":"pnn-a-novel-progressive-neural-network-for","title":"PNN: A Novel Progressive Neural Network for Fault Classification in Rotating Machinery under Small Dataset Constraint","date":"2025-03-24","arxiv_id":"2503.18263","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-fault-detection-in-co2","title":"Enhancing Fault Detection in CO2 Refrigeration Systems: Optimal Sensor Selection and Robustness Analysis Using Tree-Based Machine Learning","date":"2025-03-22","arxiv_id":"2503.17694","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-fault-detection-and-isolation-in-an","title":"Enhancing Fault Detection and Isolation in an All-Electric Auxiliary Power Unit (APU) Gas Generator by Utilizing Starter/Generator Signal","date":"2025-03-19","arxiv_id":"2503.14986","repositories_listed":0,"syntology":null},{"url":null,"slug":"defect-analysis-and-built-in-self-test-for","title":"Defect Analysis and Built-In-Self-Test for Chiplet Interconnects in Fan-out Wafer-Level Packaging","date":"2025-03-18","arxiv_id":"2503.14784","repositories_listed":0,"syntology":null},{"url":null,"slug":"identification-of-high-impedance-faults","title":"Identification of High Impedance Faults Utilizing Recurrence Plots","date":"2025-03-04","arxiv_id":"2503.02995","repositories_listed":0,"syntology":null},{"url":null,"slug":"2503-00053","title":"AI and Semantic Communication for Infrastructure Monitoring in 6G-Driven Drone Swarms","date":"2025-02-26","arxiv_id":"2503.00053","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-topology-recovery-method-for-low","title":"A Novel Topology Recovery Method for Low Voltage Distribution Networks","date":"2025-02-26","arxiv_id":"2502.18939","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-hyperparametric-itakura-saito-nmf-via","title":"Sparse Hyperparametric Itakura-Saito NMF via Bi-Level Optimization","date":"2025-02-24","arxiv_id":"2502.17123","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-bearing-fault-classification-under","title":"Multimodal Bearing Fault Classification Under Variable Conditions: A 1D CNN with Transfer Learning","date":"2025-02-23","arxiv_id":"2502.17524","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-method-for-signal-components-identification","title":"A method for signal components identification in acoustic signal with non-Gaussian background noise using clustering of data in time-frequency domain","date":"2025-02-17","arxiv_id":"2502.11786","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-machine-fault-diagnosis-a-detailed","title":"Advancing machine fault diagnosis: A detailed examination of convolutional neural networks","date":"2025-02-12","arxiv_id":"2502.08689","repositories_listed":0,"syntology":null},{"url":null,"slug":"rapid-detection-of-high-impedance-arc-faults","title":"Enhanced Rapid Detection of High-impedance Arc Faults in Medium Voltage Electrical Distribution Networks","date":"2025-02-09","arxiv_id":"2502.05846","repositories_listed":0,"syntology":null},{"url":null,"slug":"tracking-error-based-fault-tolerant-scheme","title":"Tracking Error Based Fault Tolerant Scheme for Marine Vehicles with Thruster Redundancy","date":"2025-01-31","arxiv_id":"2501.18852","repositories_listed":0,"syntology":null},{"url":null,"slug":"kkl-observer-synthesis-for-nonlinear-systems","title":"KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning","date":"2025-01-20","arxiv_id":"2501.11655","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoresttest-a-tool-for-automated-rest-api","title":"AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL","date":"2025-01-15","arxiv_id":"2501.08600","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-gnns-effective-for-multimodal-fault","title":"Are GNNs Actually Effective for Multimodal Fault Diagnosis in Microservice Systems?","date":"2025-01-06","arxiv_id":"2501.02766","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-defective-wafers-via-modular","title":"Detecting Defective Wafers Via Modular Networks","date":"2025-01-06","arxiv_id":"2501.03368","repositories_listed":0,"syntology":null},{"url":null,"slug":"gcn-abft-low-cost-online-error-checking-for","title":"GCN-ABFT: Low-Cost Online Error Checking for Graph Convolutional Networks","date":"2024-12-24","arxiv_id":"2412.18534","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-based-systems-for","title":"Deep Reinforcement Learning Based Systems for Safety Critical Applications in Aerospace","date":"2024-12-21","arxiv_id":"2412.16489","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-bearing-fault-detection","title":"Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism","date":"2024-12-15","arxiv_id":"2412.11245","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-isolation-for-the-ink-deposition","title":"Fault Isolation for the Ink Deposition Process in High-End Industrial Printers","date":"2024-12-10","arxiv_id":"2412.07545","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-agnostic-meta-learning-for-fault","title":"Model-Agnostic Meta-Learning for Fault Diagnosis of Induction Motors in Data-Scarce Environments with Varying Operating Conditions and Electric Drive Noise","date":"2024-12-05","arxiv_id":"2412.04255","repositories_listed":0,"syntology":null},{"url":null,"slug":"fd-llm-large-language-model-for-fault","title":"FD-LLM: Large Language Model for Fault Diagnosis of Machines","date":"2024-12-02","arxiv_id":"2412.01218","repositories_listed":0,"syntology":null},{"url":null,"slug":"spf-net-solar-panel-fault-detection-using-u","title":"SPF-Net: Solar panel fault detection using U-Net based deep learning image classification","date":"2024-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-attention-single-head-vision","title":"Residual Attention Single-Head Vision Transformer Network for Rolling Bearing Fault Diagnosis in Noisy Environments","date":"2024-11-27","arxiv_id":"2412.00085","repositories_listed":0,"syntology":null},{"url":null,"slug":"tailoring-the-hyperparameters-of-a-wide","title":"Finding One's Bearings in the Hyperparameter Landscape of a Wide-Kernel Convolutional Fault Detector","date":"2024-11-19","arxiv_id":"2411.15191","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fuzzy-reinforcement-lstm-based-long-term","title":"A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants","date":"2024-11-13","arxiv_id":"2411.08370","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-enhanced-inverter-fault-and-anomaly","title":"AI-Enhanced Inverter Fault and Anomaly Detection System for Distributed Energy Resources in Microgrids","date":"2024-11-13","arxiv_id":"2411.08761","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-agent-approach-for-rest-api-testing","title":"A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs","date":"2024-11-11","arxiv_id":"2411.07098","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-fault-tolerant-control-for","title":"Transformer-Based Fault-Tolerant Control for Fixed-Wing UAVs Using Knowledge Distillation and In-Context Adaptation","date":"2024-11-05","arxiv_id":"2411.02975","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-controlled-stochastic-differential","title":"Learning Controlled Stochastic Differential Equations","date":"2024-11-04","arxiv_id":"2411.01982","repositories_listed":0,"syntology":null},{"url":null,"slug":"minder-faulty-machine-detection-for-large","title":"Minder: Faulty Machine Detection for Large-scale Distributed Model Training","date":"2024-11-04","arxiv_id":"2411.01791","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-temporal-bearing-fault-detection","title":"Spatial-Temporal Bearing Fault Detection Using Graph Attention Networks and LSTM","date":"2024-10-15","arxiv_id":"2410.11923","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-high-impedance-low-current-arc","title":"Detection of High-Impedance Low-Current Arc Faults at Electrical Substations","date":"2024-10-14","arxiv_id":"2410.10151","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-network-design-and","title":"Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors","date":"2024-10-11","arxiv_id":"2410.09126","repositories_listed":0,"syntology":null},{"url":null,"slug":"mola-enhancing-industrial-process-monitoring","title":"MOLA: Enhancing Industrial Process Monitoring Using Multi-Block Orthogonal Long Short-Term Memory Autoencoder","date":"2024-10-10","arxiv_id":"2410.07508","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-fault-identification-in","title":"Deep learning-based fault identification in condition monitoring","date":"2024-10-08","arxiv_id":"2410.05889","repositories_listed":0,"syntology":null},{"url":null,"slug":"development-of-a-platform-to-enable-real-time","title":"Development of a Platform to Enable Real Time, Non-disruptive Testing and Early Fault Detection of Critical High Voltage Transformers and Switchgears in High Speed-rail","date":"2024-10-01","arxiv_id":"2410.01087","repositories_listed":0,"syntology":null},{"url":null,"slug":"xai-guided-insulator-anomaly-detection-for","title":"XAI-guided Insulator Anomaly Detection for Imbalanced Datasets","date":"2024-09-25","arxiv_id":"2409.16821","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-detection-and-identification-via","title":"Fault Detection and Identification Using a Novel Process Decomposition Algorithm for Distributed Process Monitoring","date":"2024-09-17","arxiv_id":"2409.11444","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-analysis-and-predictive-maintenance-of","title":"Fault Analysis And Predictive Maintenance Of Induction Motor Using Machine Learning","date":"2024-09-16","arxiv_id":"2409.09944","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-graph-transformer-network-for","title":"Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems","date":"2024-09-16","arxiv_id":"2409.10792","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-boosting-uncertainty-based-test","title":"FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection","date":"2024-09-13","arxiv_id":"2409.09130","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-electric-motor-damage-through","title":"Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks","date":"2024-09-12","arxiv_id":"2409.08309","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-machine-learning-for-fault-detection-in","title":"Using machine learning for fault detection in lighthouse light sensors","date":"2024-09-09","arxiv_id":"2409.05495","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifier-free-diffusion-based-weakly","title":"Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring","date":"2024-09-03","arxiv_id":"2409.01676","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-factorized-orthogonal-latent-space","title":"Learning a Factorized Orthogonal Latent Space using Encoder-only Architecture for Fault Detection; An Alarm management perspective","date":"2024-08-24","arxiv_id":"2408.13526","repositories_listed":0,"syntology":null},{"url":null,"slug":"shedad-snn-enhanced-district-heating-anomaly","title":"SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations","date":"2024-08-23","arxiv_id":"2408.14499","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-driven-transformer-model-for-fault","title":"AI-driven Transformer Model for Fault Prediction in Non-Linear Dynamic Automotive System","date":"2024-08-22","arxiv_id":"2408.12638","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-powered-dynamic-fault-detection-and","title":"AI-Powered Dynamic Fault Detection and Performance Assessment in Photovoltaic Systems","date":"2024-08-19","arxiv_id":"2409.00052","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-liu-ice-benchmark-an-industrial-fault","title":"The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study","date":"2024-08-16","arxiv_id":"2408.13269","repositories_listed":0,"syntology":null},{"url":null,"slug":"bearing-fault-diagnosis-using-graph-sampling","title":"Bearing Fault Diagnosis using Graph Sampling and Aggregation Network","date":"2024-08-12","arxiv_id":"2408.07099","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-edge-ai-system-based-on-fpga-platform-for","title":"An Edge AI System Based on FPGA Platform for Railway Fault Detection","date":"2024-08-08","arxiv_id":"2408.15245","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-rotor-fault-diagnostic-method-based-on","title":"Novel rotor fault diagnostic method based on rlmd and ht techniques","date":"2024-08-03","arxiv_id":"2408.01807","repositories_listed":0,"syntology":null},{"url":null,"slug":"2408-00033","title":"Enhanced Fault Detection and Cause Identification Using Integrated Attention Mechanism","date":"2024-07-31","arxiv_id":"2408.00033","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-maturity-framework-for-data-driven","title":"A maturity framework for data driven maintenance","date":"2024-07-26","arxiv_id":"2407.18996","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-batch-based-bearing-fault","title":"Statistical Batch-Based Bearing Fault Detection","date":"2024-07-24","arxiv_id":"2407.17236","repositories_listed":0,"syntology":null},{"url":null,"slug":"seismic-fault-sam-adapting-sam-with","title":"Seismic Fault SAM: Adapting SAM with Lightweight Modules and 2.5D Strategy for Fault Detection","date":"2024-07-19","arxiv_id":"2407.14121","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-two-stage-machine-learning-aided-approach","title":"A Two-Stage Machine Learning-Aided Approach for Quench Identification at the European XFEL","date":"2024-07-11","arxiv_id":"2407.08408","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-predictive-model-based-on-transformer-with","title":"A Predictive Model Based on Transformer with Statistical Feature Embedding in Manufacturing Sensor Dataset","date":"2024-07-09","arxiv_id":"2407.06682","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-optimizers-for-fault-isolation","title":"Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives","date":"2024-07-09","arxiv_id":"2407.06557","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-fault-detection-using-sam-with-a","title":"Unsupervised Fault Detection using SAM with a Moving Window Approach","date":"2024-07-08","arxiv_id":"2407.06303","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-task-for-fault-detection-in","title":"A Self-Supervised Task for Fault Detection in Satellite Multivariate Time Series","date":"2024-07-03","arxiv_id":"2407.02861","repositories_listed":0,"syntology":null},{"url":null,"slug":"infrared-computer-vision-for-utility-scale","title":"Infrared Computer Vision for Utility-Scale Photovoltaic Array Inspection","date":"2024-06-29","arxiv_id":"2407.00544","repositories_listed":0,"syntology":null},{"url":null,"slug":"condition-monitoring-of-wind-turbine-blades","title":"Condition monitoring of wind turbine blades via learning-based methods","date":"2024-06-28","arxiv_id":"2406.19773","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-detection-for-agents-on-power-grid","title":"Fault Detection for agents on power grid topology optimization: A Comprehensive analysis","date":"2024-06-24","arxiv_id":"2406.16426","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-gcn-a-dynamically-weighted-loss","title":"Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks","date":"2024-06-24","arxiv_id":"2406.17073","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-topological-data-analysis-for","title":"Testing Topological Data Analysis for Condition Monitoring of Wind Turbines","date":"2024-06-24","arxiv_id":"2406.16380","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-detection-in-propulsion-motors-in-the","title":"Fault detection in propulsion motors in the presence of concept drift","date":"2024-06-12","arxiv_id":"2406.08030","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-test-time-domain-adaptation-for","title":"Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions","date":"2024-06-06","arxiv_id":"2406.06607","repositories_listed":0,"syntology":null},{"url":null,"slug":"spikemm-flexi-magnification-of-high-speed","title":"SpikeMM: Flexi-Magnification of High-Speed Micro-Motions","date":"2024-06-01","arxiv_id":"2406.00383","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-machinery-fault-detection-a","title":"Data-driven Machinery Fault Detection: A Comprehensive Review","date":"2024-05-29","arxiv_id":"2405.18843","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-voltage-analysis-and-patterns-in","title":"Differential Voltage Analysis and Patterns in Parallel-Connected Pairs of Imbalanced Cells","date":"2024-05-28","arxiv_id":"2405.17754","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-visual-fault-detection-for-freight-1","title":"Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness","date":"2024-05-27","arxiv_id":"2405.17004","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-real-nvp-for-satellite-power","title":"Physics-Informed Real NVP for Satellite Power System Fault Detection","date":"2024-05-27","arxiv_id":"2405.17339","repositories_listed":0,"syntology":null},{"url":null,"slug":"pattern-based-time-series-risk-scoring-for","title":"Pattern-Based Time-Series Risk Scoring for Anomaly Detection and Alert Filtering -- A Predictive Maintenance Case Study","date":"2024-05-24","arxiv_id":"2405.17488","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-active-learning-framework-with-a-class","title":"An Active Learning Framework with a Class Balancing Strategy for Time Series Classification","date":"2024-05-20","arxiv_id":"2405.12122","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-protection-and-fault","title":"Machine Learning-Based Protection and Fault Identification of 100% Inverter-Based Microgrids","date":"2024-05-12","arxiv_id":"2405.07310","repositories_listed":0,"syntology":null},{"url":null,"slug":"fault-detection-and-monitoring-using-an","title":"Fault Detection and Monitoring using a Data-Driven Information-Based Strategy: Method, Theory, and Application","date":"2024-05-06","arxiv_id":"2405.03667","repositories_listed":0,"syntology":null},{"url":null,"slug":"closed-loop-sensitivity-identification-for","title":"Closed-Loop Sensitivity Identification for Cross-Directional Systems","date":"2024-05-02","arxiv_id":"2405.01094","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-in-short-reach-optical","title":"Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey","date":"2024-05-02","arxiv_id":"2405.09557","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-layer-deep-learning-network-random","title":"Three-layer deep learning network random trees for fault detection in chemical production process","date":"2024-05-01","arxiv_id":"2405.00311","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-of-uncorrelated-residual-variables","title":"Generation of Uncorrelated Residual Variables for Chemical Process Fault Diagnosis via Transfer Learning-based Input-Output Decoupled Network","date":"2024-04-29","arxiv_id":"2404.18528","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-fault-detection-and-diagnosis-for","title":"Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis","date":"2024-04-24","arxiv_id":"2404.15609","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoencoder-assisted-feature-ensemble-net-for","title":"Autoencoder-assisted Feature Ensemble Net for Incipient Faults","date":"2024-04-22","arxiv_id":"2404.13941","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-artificial-intelligence-6","title":"Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review","date":"2024-04-17","arxiv_id":"2404.11597","repositories_listed":0,"syntology":null},{"url":null,"slug":"care-to-compare-a-real-world-dataset-for","title":"CARE to Compare: A real-world dataset for anomaly detection in wind turbine data","date":"2024-04-16","arxiv_id":"2404.10320","repositories_listed":0,"syntology":null}],"record_sha256":"442e042222612ef6cc06d5630cf6ca7ada9cb683636eda4e527b2f5fa153526d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}