Machine Learning-based Monitoring

Machine Learning-Based Monitoring is a condition monitoring approach that employs machine learning algorithms to automatically detect, diagnose, or predict machine faults by learning patterns from operational data, distinguishing it from traditional rule-based or signal-processing-based methods through its reliance on data-driven model adaptation.

506

Publications

23.8K

Citations

1.8K

Authors

726

Institutions

Publications per year

2017–2026

442

Authors

1.8K

  1. FG
    Furong Gao

    Hong Kong University of Science and Technology, Hong Kong SAR China

    5 publications · 250 citations

    5

    h-index

  2. EK
    Evgeny Kharlamov

    University of Oslo, Norway

    3 publications · 159 citations

    4

    h-index

  3. VS
    V. Sugumaran

    Vellore Institute of Technology University, India

    3 publications · 81 citations

    4

    h-index

  4. HA
    Husnain Ali

    Hong Kong University of Science and Technology, Hong Kong SAR China

    4 publications · 178 citations

    4

    h-index

  5. BZ
    Baifan Zhou

    Robert Bosch (Germany), Germany

    3 publications · 159 citations

    4

    h-index

1–5 of 1.8K

Venues

  1. Sensors

    13 publications · 514 citations

    11

    h-index

  2. Energies

    10 publications · 344 citations

    11

    h-index

  3. Applied Sciences

    9 publications · 357 citations

    10

    h-index

  4. IEEE Access

    6 publications · 222 citations

    9

    h-index

  5. IEEE Transactions on Industrial Informatics

    6 publications · 529 citations

    7

    h-index

1–5