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

Abstract:

This paper presents a mathematical framework for modelingprognostics at a system level, by combining the prognosticsprinciples with the Plausible Petri nets (PPNs) formalism,first developed in M. Chiachio et al. [Proceedings of theFuture Technologies Conference, San Francisco, (2016), pp.165-172]. The main feature of the resulting framework residesin its efficiency to jointly consider the dynamics of discreteevents, like maintenance actions, together with multiplesources of uncertain information about the system state likethe probability distribution of end-of-life, information fromsensors, and information coming from expert knowledge. Inaddition, the proposed methodology allows us to rigorouslymodel the flow of information through logic operations, thusmaking it useful for nonlinear control, Bayesian updating,and decision making. A degradation process of an engineeringsub-system is analyzed as an example of application usingcondition-based monitoring from sensors, pbkp_redicted statesfrom prognostics algorithms, along with information comingfrom expert knowledge. The numerical results reveal how theinformation from sensors and prognostics algorithms can beprocessed, transferred, stored, and integrated with discreteeventmaintenance activities for nonlinear control operationsat system level.