Corpus ID: 1396821

Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction

@article{Yoon2017SemisupervisedLW,
  title={Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction},
  author={Andre S. Yoon and Taehoon Lee and Yongsub Lim and Deokwoo Jung and Philgyun Kang and Dongwon Kim and Keuntae Park and Yongjin Choi},
  journal={ArXiv},
  year={2017},
  volume={abs/1709.00845}
}
  • Andre S. Yoon, Taehoon Lee, +5 authors Yongjin Choi
  • Published 2017
  • Computer Science, Mathematics
  • ArXiv
  • This work presents a novel semi-supervised learning approach for data-driven modeling of asset failures when health status is only partially known in historical data. We combine a generative model parameterized by deep neural networks with non-linear embedding technique. It allows us to build prognostic models with the limited amount of health status information for the precise prediction of future asset reliability. The proposed method is evaluated on a publicly available dataset for remaining… CONTINUE READING

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 17 CITATIONS

    An Adversarial Learning Approach for Machine Prognostic Health Management

    VIEW 1 EXCERPT
    CITES METHODS

    Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling

    VIEW 3 EXCERPTS
    CITES METHODS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 35 REFERENCES

    A modified echo state network based remaining useful life estimation approach

    VIEW 1 EXCERPT

    RUL prediction based on a new similarity-instance based approach

    VIEW 1 EXCERPT

    Deep Learning and Its Applications to Machine Health Monitoring: A Survey

    VIEW 1 EXCERPT

    Auto-Encoding Variational Bayes

    VIEW 7 EXCERPTS
    HIGHLY INFLUENTIAL