Health Monitoring of a Planetary Rover Using Hybrid Particle Petri Nets

Application and Theory of Petri Nets and Concurrency - Lecture Notes in Computer Science ◽

10.1007/978-3-319-39086-4_13 ◽

2016 ◽

pp. 196-215 ◽

Cited By ~ 1

Author(s):

Quentin Gaudel ◽

Pauline Ribot ◽

Elodie Chanthery ◽

Matthew J. Daigle

Keyword(s):

Petri Nets ◽

Health Monitoring ◽

Planetary Rover ◽

Hybrid Particle

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Diagnosis of Hybrid Systems Using Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover

Fault Diagnosis of Hybrid Dynamic and Complex Systems ◽

10.1007/978-3-319-74014-0_9 ◽

2018 ◽

pp. 209-241

Author(s):

Quentin Gaudel ◽

Elodie Chanthery ◽

Pauline Ribot ◽

Matthew J. Daigle

Keyword(s):

Petri Nets ◽

Hybrid Systems ◽

Planetary Rover ◽

Hybrid Particle

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Hybrid Particle Petri Nets for Systems Health Monitoring under Uncertainty

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2015.v6i4.2323 ◽

2020 ◽

Vol 6 (4) ◽

Author(s):

Quentin Gaudel ◽

Elodie Chanthery ◽

Pauline Ribot

Keyword(s):

Petri Nets ◽

Hybrid Systems ◽

Health Monitoring ◽

Particle Filtering ◽

Experimental Results ◽

Hybrid Technique ◽

Discrete Events ◽

Hybrid Particle ◽

Continuous State ◽

Tank System

This paper focuses on how to treat uncertainty in health monitoring of hybrid systems by using a model-based method. The Hybrid Particle Petri Nets (HPPN) formalism is definedin the context of health monitoring to model hybrid systems and to generate diagnosers of such systems. The main advantage of this formalism is that it takes into account knowledgebased uncertainty and uncertainty in diagnosis process. The HPPN-based diagnoser deals with occurrences of unobservable discrete events (such as faults) and is robust to false observations. It also estimates the continuous state of the system by using particle filtering. Finally, HPPN can represent the system degradation that is often dealt with using probabilistic tools. A hybrid technique is thus used to group all this knowledge and to deduce the diagnosis results. The approach is demonstrated on a three-tank system. Experimental results are given, illustrating how different kinds of uncertainty are taken into account when using HPPN.

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