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

Manuel Chiachio, Juan Chiachio, Shankar Sankararaman, and John Andrews
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Lloyd's Register Foundation
phmc_17_055.pdf475.19 KBSeptember 7, 2017 - 10:18am

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. Chiachio 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.

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Publication Volume: 
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Submission Keywords: 
system-level PHM
Prognostic Information Management
Petri nets
Submission Topic Areas: 
Modeling and simulation
Uncertainty Quantification and Management in PHM

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