Semi-supervised constrained hidden markov model using multiple sensors for remaining useful life prediction and optimal predictive maintenance

Xinyu Zhao, Yunyi Kang, Hao Yan, Feng Ju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we propose a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is the first in the literature to simultaneously address the challenges of semisupervised setting, left-to-right constraint, and monotonicity constraint in a multiple-sensor setting. This proposed method is also effective in estimating the RUL while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov Decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
EditorsN. Scott Clements, Bin Zhang, Abhinav Saxena
PublisherPrognostics and Health Management Society
Edition1
ISBN (Electronic)9781936263059
DOIs
StatePublished - Sep 23 2019
Event11th Annual Conference of the Prognostics and Health Management Society, PHM 2019 - Scottsdale, United States
Duration: Sep 23 2019Sep 26 2019

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Number1
Volume11
ISSN (Print)2325-0178

Conference

Conference11th Annual Conference of the Prognostics and Health Management Society, PHM 2019
CountryUnited States
CityScottsdale
Period9/23/199/26/19

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications

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  • Cite this

    Zhao, X., Kang, Y., Yan, H., & Ju, F. (2019). Semi-supervised constrained hidden markov model using multiple sensors for remaining useful life prediction and optimal predictive maintenance. In N. S. Clements, B. Zhang, & A. Saxena (Eds.), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM (1 ed.). (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Vol. 11, No. 1). Prognostics and Health Management Society. https://doi.org/10.36001/phmconf.2019.v11i1.851