{"url":"/task/fault-detection","name":"Fault Detection","slug":"fault-detection","description_markdown":null,"categories":[{"name":"Miscellaneous","url":"/area/miscellaneous"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":511,"papers_with_code":80,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":7,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[{"url":"/dataset/pronostia-bearing-dataset","name":"PRONOSTIA Bearing Dataset","full_name":"","num_papers_in_archive":6},{"url":"/dataset/paderbone-university-bearing-fault-benckmark","name":"Paderbone University Bearing Fault Benckmark","full_name":"","num_papers_in_archive":3},{"url":"/dataset/ims-bearing-dataset","name":"IMS Bearing Dataset","full_name":"","num_papers_in_archive":2},{"url":"/dataset/unbalance-classification-using-vibration-data","name":"Unbalance Classification Using Vibration Data","full_name":"Vibration Measurements on a Rotating Shaft at Different Unbalance Strengths","num_papers_in_archive":2},{"url":"/dataset/centrifugal-pump-fault-detection","name":"Centrifugal Pump Fault Detection","full_name":"","num_papers_in_archive":1},{"url":"/dataset/pronto","name":"PRONTO","full_name":"PRONTO heterogeneous benchmark dataset","num_papers_in_archive":1},{"url":"/dataset/k-drone","name":"Sound-based drone fault classification using multitask learning","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":80,"tagged_in_all":511,"items":[{"url":"/paper/online-forecasting-and-anomaly-detection","title":"Online Forecasting and Anomaly Detection Based on the ARIMA Model","date":"2021-04-02","arxiv_id":null,"repositories_listed":3,"syntology":null},{"url":"/paper/bearing-fault-diagnosis-base-on-multi-scale","title":"Bearing Fault Diagnosis Base on Multi-scale CNN and LSTM Model","date":"2020-06-05","arxiv_id":null,"repositories_listed":3,"syntology":null},{"url":"/paper/self-supervised-log-parsing","title":"Self-Supervised Log Parsing","date":"2020-03-17","arxiv_id":"2003.07905","repositories_listed":3,"syntology":null},{"url":"/paper/anomaly-detection-in-ir-images-of-pv-modules","title":"Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning","date":"2021-12-06","arxiv_id":"2112.02922","repositories_listed":2,"syntology":null},{"url":"/paper/time-series-representation-learning-via","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","date":"2021-06-26","arxiv_id":"2106.14112","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/gpla-12-an-acoustic-signal-dataset-of-gas","title":"GPLA-12: An Acoustic Signal Dataset of Gas Pipeline Leakage","date":"2021-06-19","arxiv_id":"2106.10277","repositories_listed":2,"syntology":null},{"url":"/paper/testing-with-fewer-resources-an-adaptive","title":"Testing with Fewer Resources: An Adaptive Approach to Performance-Aware Test Case Generation","date":"2019-07-19","arxiv_id":"1907.08578","repositories_listed":2,"syntology":null},{"url":"/paper/alfa-a-dataset-for-uav-fault-and-anomaly","title":"ALFA: A Dataset for UAV Fault and Anomaly Detection","date":"2019-07-14","arxiv_id":"1907.06268","repositories_listed":2,"syntology":null},{"url":"/paper/online-isolation-forest","title":"Online Isolation Forest","date":"2025-05-14","arxiv_id":"2505.09593","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/comparative-study-of-generative-models-for","title":"Comparative Study of Generative Models for Early Detection of Failures in Medical Devices","date":"2025-05-07","arxiv_id":"2505.04845","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-traditional-machine-learning-and","title":"Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers","date":"2025-05-07","arxiv_id":"2505.06295","repositories_listed":1,"syntology":null},{"url":"/paper/data-driven-sensor-fault-diagnosis-with","title":"Data-Driven Sensor Fault Diagnosis with Proven Guarantees using Incrementally Stable Recurrent Neural Networks","date":"2025-04-28","arxiv_id":"2504.19688","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-anomaly-detection-through-mass","title":"Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport","date":"2025-02-18","arxiv_id":"2502.12793","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/integrating-physics-and-data-driven","title":"Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction","date":"2025-02-11","arxiv_id":"2502.07344","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-annealing-for-robust-principal","title":"Quantum Annealing for Robust Principal Component Analysis","date":"2025-01-11","arxiv_id":"2501.10431","repositories_listed":1,"syntology":null},{"url":"/paper/faultexplainer-leveraging-large-language","title":"FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis","date":"2024-12-19","arxiv_id":"2412.14492","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-fault-and-severity-classification","title":"Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks","date":"2024-12-02","arxiv_id":"2412.01322","repositories_listed":1,"syntology":null},{"url":"/paper/wind-turbine-condition-monitoring-based-on","title":"Wind turbine condition monitoring based on intra- and inter-farm federated learning","date":"2024-09-05","arxiv_id":"2409.03672","repositories_listed":1,"syntology":null},{"url":"/paper/learning-what-to-monitor-using-machine","title":"Learning what to Monitor: using Machine Learning to Improve Past STL Monitoring","date":"2024-08-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/wideband-relative-transfer-function-rtf","title":"Wideband Relative Transfer Function (RTF) Estimation Exploiting Frequency Correlations","date":"2024-07-19","arxiv_id":"2407.14152","repositories_listed":1,"syntology":null},{"url":"/paper/lithium-ion-battery-system-health-monitoring","title":"Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data","date":"2024-06-27","arxiv_id":"2406.19015","repositories_listed":1,"syntology":null},{"url":"/paper/dkdl-net-a-lightweight-bearing-fault","title":"DKDL-Net: A Lightweight Bearing Fault Detection Model via Decoupled Knowledge Distillation and Low-Rank Adaptation Fine-tuning","date":"2024-06-10","arxiv_id":"2406.06653","repositories_listed":1,"syntology":null},{"url":"/paper/multi-scale-quaternion-cnn-and-bigru-with","title":"Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing","date":"2024-05-25","arxiv_id":"2405.16114","repositories_listed":1,"syntology":null},{"url":"/paper/usd-unsupervised-soft-contrastive-learning","title":"USD: Unsupervised Soft Contrastive Learning for Fault Detection in Multivariate Time Series","date":"2024-05-25","arxiv_id":"2405.16258","repositories_listed":1,"syntology":null},{"url":"/paper/deephydra-resource-efficient-time-series","title":"DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems","date":"2024-05-13","arxiv_id":"2405.07749","repositories_listed":1,"syntology":null},{"url":"/paper/fault-identification-enhancement-with","title":"Fault Identification Enhancement with Reinforcement Learning (FIERL)","date":"2024-05-08","arxiv_id":"2405.04938","repositories_listed":1,"syntology":null},{"url":"/paper/a-probabilistic-estimation-of-remaining","title":"A probabilistic estimation of remaining useful life from censored time-to-event data","date":"2024-05-02","arxiv_id":"2405.01614","repositories_listed":1,"syntology":null},{"url":"/paper/tfpred-learning-discriminative","title":"TFPred: Learning Discriminative Representations from Unlabeled Data for Few-Label Rotating Machinery Fault Diagnosis","date":"2024-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generalised-envelope-spectrum-based-signal-to","title":"Generalised envelope spectrum-based signal-to-noise objectives: Formulation, optimisation and application for gear fault detection under time-varying speed conditions","date":"2024-04-26","arxiv_id":"2405.00727","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-attacks-and-defenses-in-automated","title":"Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process","date":"2024-03-20","arxiv_id":"2403.13502","repositories_listed":1,"syntology":null}],"syntology_records":3,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}