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An Adaptive Prognostic Methodology and System Framework for Engineering Systems under Dynamic Working Regimes

Yang, Shanhu
http://orcid.org/0000-0002-5562-9645
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1455209450

Abstract Details

2016, PhD, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Prognostics and Health Management (PHM) as a research discipline focuses on assessing degradation behavior and predicting time to failure of an engineering system with condition monitoring data collected throughout the lifespan of the system. The information of predicted Remaining Useful Life (RUL) and potential failure modes further enables just-in-time maintenance, reduced operational cost and optimized production. In recent years with the development of information systems such as cloud computing and Internet of Things (IOT), machine data from factory floors can be collected more conveniently with higher speed, volume and variety, which brings about new opportunities and much wider application of PHM technologies. On the other hand, the emerging industrial big data with real world complications also imposes greater challenges to the PHM research community. Data collected from a large amount of machine units under dynamic working regimes requires algorithms to adaptively and autonomously recognize and handle different situations. Autonomous PHM algorithms can further be implemented in centralized computing platforms for more efficient, faster and large scale data mining and analytics, which will eventually lead to more effective handling and exploitation of industrial big data. PHM algorithms have been developed based on specific applications and datasets. In addition, most of PHM tools are developed based on limited working regimes. In reality, many engineered machinery and systems often work under different dynamic working regimes and as a consequence it is always a challenge to implement PHM in such conditions. This dissertation work presents the development of a systematically designed and implementation-ready methodology for adaptive health assessment and prognostics for real world machine fleets that undergo dynamic working regimes and other complications. Due to limitations in data and knowledge for in-field systems, the approach assumes no prior knowledge or available training data and attempts to extract degradation information only from condition monitoring data streamed in real time. The approach contains a generalized state space model for machine degradation and an adaptive and online methodology for real time degradation assessment and prediction. The degradation model is a generalized yet comprehensive description of the relationships among the three key aspects in the PHM related research, which are system degradation, system measurements and working regimes. The online methodology further consists of an adaptive segmentation method for identification of health stages based on local variation, a variable selection algorithm for selecting related working regime parameters and an Adaptive Kalman Filter (AKF) based online filtering method for model identification and prediction. The methodology is demonstrated and validated using both simulated data and data from real world industrial applications. The case studies show that the proposed approach is able to deliver robust and accurate results with little algorithm tuning needed for different applications, which is ideal for facilitating automated data processing and analytics in online PHM platforms.
Jay Lee, Ph.D. (Committee Chair)
Rajkumar Roy, Ph.D. (Committee Member)
Thomas Richard Huston, Ph.D. (Committee Member)
J. Kim, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
150 p.
Engineering, Mechanical; Mechanics
Prognostics and Health Management; Adaptive Learning; Accelerated Life Testing; Cloud Computing

Recommended Citations

Citations

  • Yang, S. (2016). An Adaptive Prognostic Methodology and System Framework for Engineering Systems under Dynamic Working Regimes [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1455209450

    APA Style (7th edition)

  • Yang, Shanhu. An Adaptive Prognostic Methodology and System Framework for Engineering Systems under Dynamic Working Regimes. 2016. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1455209450.

    MLA Style (8th edition)

  • Yang, Shanhu. "An Adaptive Prognostic Methodology and System Framework for Engineering Systems under Dynamic Working Regimes." Doctoral dissertation, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1455209450

    Chicago Manual of Style (17th edition)

ucin1455209450
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© 2016, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.