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A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift

Jin, Chao
http://orcid.org/0000-0002-7237-981X
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969

Abstract Details

2015, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
In the field of prognostics and health management, process monitoring is an essential technique to equip the system with the intelligence of being “aware” of any faults. Owing to tool fatigue, upstream material variation and electronic component drift, machine characteristics will often shift from initial states. As a result, sensor signals collected from the same equipment will possess varying correlation structures and offset in distributions, even if the health condition does not change. In order to build an effective data-driven process monitoring model, the constructed model has to be able to robustly differentiate the drifting healthy states from faulty conditions. In this thesis, a sequential process monitoring approach using hidden Markov model is proposed for process monitoring to overcome influences of such drifts. During training stage, a discrete hidden Markov model is constructed using only healthy condition data. A health threshold is determined based on the deviation of normal condition health index, which is the normalized slope of negative log-likelihood. During monitoring stage, the health index of the new process from the same machine is calculated. Faults will be detected when the metric goes beyond the threshold. The developed approach has been validated using a case study for semiconductor etching process. And result of the proposed approach is benchmarked with both global and regime-specific local models using principal component analysis and self-organizing maps.
Jay Lee, Ph.D. (Committee Chair)
J. Kim, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
94 p.
Mechanics
process monitoring; hidden Markov model; fault detection

Recommended Citations

Citations

  • Jin, C. (2015). A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969

    APA Style (7th edition)

  • Jin, Chao. A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift. 2015. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969.

    MLA Style (8th edition)

  • Jin, Chao. "A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift." Master's thesis, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969

    Chicago Manual of Style (17th edition)

ucin1445341969
811
© 2015, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.