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Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment

Feng, Jianshe
http://orcid.org/0000-0003-1832-9739
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267012325542

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Prognostics and health management (PHM) has gradually become an essential technique to improve the availability and efficiency of a complex system. With the rapid advancement of sensor technology and communication technology, a huge amount of real-time data are generated from various industrial applications, which brings new challenges to PHM in the context of big data streams. On one hand, high-volume stream data places a heavy demand on data storage, communication, and PHM modeling. On the other hand, continuous change and drift are essential properties of stream data in an evolving environment, which requires the PHM model to be capable to capture the new information in stream data adaptively, efficiently and continuously. This research proposes a systematic methodology to develop an effective online learning PHM with adaptive sampling techniques to fuse information from continuous stream data. An adaptive sample selection strategy is developed so that the representative samples can be effectively selected in both off-line and online environment. In addition, various data-driven models, including probabilistic models, Bayesian algorithms, incremental methods, and ensemble algorithms, are employed and integrated into the proposed methodology for model establishment and updating with important samples selected from streaming sequence. Finally, the effectiveness of proposed systematic methodology is validated with four typical industrial applications including power forecasting of a combined cycle power plant, fault detection of hard disk drive, virtual metrology in semiconductor manufacturing processes, and prognosis of battery state of capacity. The result comparison between the proposed methodology and state-of-art benchmark methods indicates that the proposed methodology is capable to build an adaptive PHM with sustainable performance to deal with dynamic issues in processes, which provides a promising way to prolong the PHM model lifetime after implementation.
Jay Lee, Ph.D. (Committee Chair)
Hossein Davari Ardakani, Ph.D. (Committee Member)
Thomas Richard Huston, Ph.D. (Committee Member)
Manish Kumar, Ph.D. (Committee Member)
Zonghchang Liu, Ph.D. (Committee Member)
Jing Shi, Ph.D. (Committee Member)
194 p.
Engineering
adaptive PHM; sample selection; sample importance test; online modeling; sequential model updating

Recommended Citations

Citations

  • Feng, J. (2020). Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267012325542

    APA Style (7th edition)

  • Feng, Jianshe. Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267012325542.

    MLA Style (8th edition)

  • Feng, Jianshe. "Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267012325542

    Chicago Manual of Style (17th edition)

ucin1593267012325542
1,197
© 2020, some rights reserved.Creative Commons License Methodology of Adaptive Prognostics and Health Management in Dynamic Work Environment by Jianshe Feng is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.