Bayesian Prognostics

Bayesian Prognostics is a probabilistic framework for predictive maintenance that employs Bayesian inference to update the degradation state and predict the remaining useful life of a system by sequentially incorporating observed condition monitoring data.

218

Publications

13.9K

Citations

583

Authors

263

Institutions

Publications per year

2017–2026

95

Authors

583

  1. KG
    Kai Goebel

    Ames Research Center, United States

    14 publications · 1.7K citations

    12

    h-index

  2. XS
    Xiaosheng Si

    Xi'an High Tech University, China

    7 publications · 332 citations

    7

    h-index

  3. ME
    Marcos E. Orchard

    University of Chile, Chile

    6 publications · 313 citations

    6

    h-index

  4. BS
    Bhaskar Saha

    Ames Research Center, United States

    7 publications · 1.3K citations

    6

    h-index

  5. NG
    Nagi Gebraeel

    Georgia Institute of Technology, United States

    6 publications · 1.3K citations

    6

    h-index

1–5 of 583

Venues

  1. Reliability Engineering & System Safety

    22 publications · 1.6K citations

    21

    h-index

  2. IEEE Transactions on Reliability

    19 publications · 830 citations

    16

    h-index

  3. IEEE Access

    9 publications · 248 citations

    9

    h-index

  4. Mechanical Systems and Signal Processing

    MSSP · 7 publications · 721 citations

    7

    h-index

  5. International Journal of Prognostics and Health Management

    IJPHM · 6 publications · 192 citations

    6

    h-index

1–5