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T09. Industrial time series, anomaly detection, and predictive maintenance

Sensor-driven fault detection, remaining useful life, and condition monitoring research.

  1. T09-1Similar problem

    Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress

    Authors
    Renjie Wu, Eamonn J. Keogh
    Year
    2023
    Venue
    IEEE Transactions on Knowledge and Data Engineering vol. 35 no. 3 pp. 2421 pp. 2429

    Korean review of T09-1

  2. T09-2Similar problem

    Towards a Rigorous Evaluation of Time-series Anomaly Detection

    Authors
    Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon
    Year
    2022
    Venue
    AAAI 2022

    Korean review of T09-2

  3. T09-3Similar problem

    The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark

    Authors
    Qinghua Liu, John Paparrizos
    Year
    2024
    Venue
    NeurIPS 2024 Datasets and Benchmarks Track

    Korean review of T09-3

  4. T09-4Similar problem

    Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection

    Authors
    John Paparrizos, Paul Boniol, Themis Palpanas, Ruey S. Tsay, Aaron Elmore, Michael J. Franklin
    Year
    2022
    Venue
    Proceedings of the VLDB Endowment vol. 15 no. 11 pp. 2774 pp. 2787

    Korean review of T09-4

  5. T09-5Similar problem

    A Review on Outlier/Anomaly Detection in Time Series Data

    Authors
    Ane Blazquez-Garcia, Angel Conde, Usue Mori, Jose A. Lozano
    Year
    2021
    Venue
    ACM Computing Surveys vol. 54 no. 3 56, pp. 1 pp. 33

    Korean review of T09-5

  6. T09-7Similar problem

    Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

    Authors
    Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, Tom Soderstrom
    Year
    2018
    Venue
    KDD 2018

    Korean review of T09-7

  7. T09-9Similar problem

    A Dataset to Support Research in the Design of Secure Water Treatment Systems (SWaT)

    Authors
    Jonathan Goh, Sridhar Adepu, Khurum Nazir Junejo, Aditya Mathur
    Year
    2017
    Venue
    CRITIS 2016

    Korean review of T09-9

  8. T09-13Similar problem

    Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation

    Authors
    Abhinav Saxena, Kai Goebel, Don Simon, Neil Eklund
    Year
    2008
    Venue
    2008 International Conference on Prognostics and Health Management (IEEE)

    Korean review of T09-13

  9. T09-14Similar problem

    A review on machinery diagnostics and prognostics implementing condition-based maintenance

    Authors
    Andrew K. S. Jardine, Daming Lin, Dragan Banjevic
    Year
    2006
    Venue
    Mechanical Systems and Signal Processing vol. 20 no. 7 pp. 1483 pp. 1510

    Korean review of T09-14

  10. T09-15Similar problem

    Machinery health prognostics: A systematic review from data acquisition to RUL prediction

    Authors
    Yaguo Lei, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, Jing Lin
    Year
    2018
    Venue
    Mechanical Systems and Signal Processing vol. 104 pp. 799 pp. 834

    Korean review of T09-15

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