Data-driven Prognostics

Data-Driven Prognostics is a predictive methodology within prognostics and health management that fundamentally relies on empirical data and statistical or machine learning models, rather than physics-based models, to estimate the remaining useful life of systems or components.

273

Publications

17.9K

Citations

881

Authors

394

Institutions

Publications per year

2017–2026

174

Authors

881

  1. NZ
    Noureddine Zerhouni

    Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies, France

    15 publications · 1.3K citations

    14

    h-index

  2. RG
    Rafael Gouriveau

    Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies, France

    10 publications · 986 citations

    10

    h-index

  3. KJ
    Kamran Javed

    Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies, France

    7 publications · 831 citations

    7

    h-index

  4. KG
    Kai Goebel

    Ames Research Center, United States

    7 publications · 1K citations

    7

    h-index

  5. PW
    Pingfeng Wang

    Wichita State University, United States

    6 publications · 659 citations

    6

    h-index

1–5 of 881

Venues

  1. Reliability Engineering & System Safety

    17 publications · 1.8K citations

    15

    h-index

  2. Mechanical Systems and Signal Processing

    MSSP · 8 publications · 709 citations

    8

    h-index

  3. IEEE Access

    8 publications · 158 citations

    8

    h-index

  4. International Journal of Prognostics and Health Management

    IJPHM · 6 publications · 258 citations

    7

    h-index

  5. IEEE Transactions on Industrial Electronics

    7 publications · 1.4K citations

    7

    h-index

1–5