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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. T09-6Structural parallel

    Anomaly Detection in Time Series: A Comprehensive Evaluation

    Authors
    Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock
    Year
    2022
    Venue
    Proceedings of the VLDB Endowment vol. 15 no. 9 pp. 1779 pp. 1797

    Korean review of T09-6

  7. 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

  8. T09-8Structural parallel

    Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network (OmniAnomaly)

    Authors
    Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, Dan Pei
    Year
    2019
    Venue
    KDD 2019

    Korean review of T09-8

  9. 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

  10. T09-10Structural parallel

    Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

    Authors
    Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long
    Year
    2022
    Venue
    ICLR 2022 (Spotlight)

    Korean review of T09-10

  11. T09-11Structural parallel

    TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

    Authors
    Shreshth Tuli, Giuliano Casale, Nicholas R. Jennings
    Year
    2022
    Venue
    Proceedings of the VLDB Endowment vol. 15 no. 6 pp. 1201 pp. 1214

    Korean review of T09-11

  12. T09-12Structural parallel

    Time-Series Anomaly Detection Service at Microsoft

    Authors
    Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, Qi Zhang
    Year
    2019
    Venue
    KDD 2019

    Korean review of T09-12

  13. 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

  14. 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

  15. 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

  16. T09-16Candidate approach

    Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects

    Authors
    Oscar Serradilla, Ekhi Zugasti, Jon Rodriguez, Urko Zurutuza
    Year
    2022
    Venue
    Applied Intelligence vol. 52 no. 10 pp. 10934 pp. 10964

    Korean review of T09-16

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