Integration of prognostics at a system level: a Petri net approach
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. 165-1...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/48654/ |
| _version_ | 1848797816196956160 |
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| author | Chiachío, Manuel Chiachío, Juan Sankararaman, Shankar Andrews, John |
| author_facet | Chiachío, Manuel Chiachío, Juan Sankararaman, Shankar Andrews, John |
| author_sort | Chiachío, Manuel |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:09:53Z |
| format | Conference or Workshop Item |
| id | nottingham-48654 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:09:53Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-486542020-05-04T19:10:25Z https://eprints.nottingham.ac.uk/48654/ Integration of prognostics at a system level: a Petri net approach Chiachío, Manuel Chiachío, Juan Sankararaman, Shankar Andrews, John 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. 2017-10-02 Conference or Workshop Item PeerReviewed Chiachío, Manuel, Chiachío, Juan, Sankararaman, Shankar and Andrews, John (2017) Integration of prognostics at a system level: a Petri net approach. In: 2017 Annual Conference of the Prognostics and Health Management Society, 2-5 Oct 2017, St. Petersburg, Florida, USA. https://www.phmsociety.org/events/conference/phm/17/proceedings |
| spellingShingle | Chiachío, Manuel Chiachío, Juan Sankararaman, Shankar Andrews, John Integration of prognostics at a system level: a Petri net approach |
| title | Integration of prognostics at a system level: a Petri net approach |
| title_full | Integration of prognostics at a system level: a Petri net approach |
| title_fullStr | Integration of prognostics at a system level: a Petri net approach |
| title_full_unstemmed | Integration of prognostics at a system level: a Petri net approach |
| title_short | Integration of prognostics at a system level: a Petri net approach |
| title_sort | integration of prognostics at a system level: a petri net approach |
| url | https://eprints.nottingham.ac.uk/48654/ https://eprints.nottingham.ac.uk/48654/ |