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Integration of prognostics at a system level: a Petri net approach

Chiach�o, Manuel; Chiach�o, Juan; Sankararaman, Shankar; Andrews, John

Authors

Manuel Chiach�o

Juan Chiach�o

Shankar Sankararaman

JOHN ANDREWS john.andrews@nottingham.ac.uk
Professor of Infrastructure Asset Management



Abstract

This paper presents a mathematical framework for modeling prognostics at a system level, by combining the prognostics principles with the Plausible Petri nets (PPNs) formalism, first developed in M. Chiach´ıo et al. [Proceedings of the Future Technologies Conference, San Francisco, (2016), pp. 165-172]. The main feature of the resulting framework resides in its efficiency to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about the system state like the probability distribution of end-of-life, information from sensors, and information coming from expert knowledge. In addition, the proposed methodology allows us to rigorously model the flow of information through logic operations, thus making it useful for nonlinear control, Bayesian updating, and decision making. A degradation process of an engineering sub-system is analyzed as an example of application using condition-based monitoring from sensors, predicted states from prognostics algorithms, along with information coming from expert knowledge. The numerical results reveal how the information from sensors and prognostics algorithms can be processed, transferred, stored, and integrated with discrete-event maintenance activities for nonlinear control operations at system level.

Citation

Chiachío, M., Chiachío, J., Sankararaman, S., & Andrews, J. (2017). Integration of prognostics at a system level: a Petri net approach.

Conference Name 2017 Annual Conference of the Prognostics and Health Management Society
End Date Oct 5, 2017
Acceptance Date Jun 30, 2017
Publication Date Oct 2, 2017
Deposit Date Dec 11, 2017
Publicly Available Date Mar 29, 2024
Peer Reviewed Peer Reviewed
Public URL https://nottingham-repository.worktribe.com/output/886071
Publisher URL https://www.phmsociety.org/events/conference/phm/17/proceedings
Additional Information Published in: Proceedings of the 2017 Annual Conference of the Prognostics and Health Management Society. isbn: 9781936263264

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