Pauline Ribot

Diagnosis and prognosis for the maintenance of complex systems

This paper adresses the problem of maintenance of a complex and heterogeneous system like an aircraft. To optimise maintenance, it is required to embed in the system a health monitoring system that implements diagnostic and prognostic capabilities. This paper thus presents a formal characterisation of the diagnostic and prognostic problems in order to support the maintenance of a complex system.

Health Monitoring of a Planetary Rover Using Hybrid Particle Petri Nets

This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype. The diagnosis is performed by using a model-based method in the context of health management of hybrid systems. In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

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

This chapter presents a new methodology to perform health monitoring of hybrid systems under uncertainty. Hybrid systems can be represented as multi-mode systems with hybrid automata. Diagnosers are generated from these hybrid automata using a new data structure in order to monitor both the behavior and degradation of such systems. After a review of the state of the art on different existing solutions for diagnosis of hybrid systems under uncertainty, we propose to introduce the Hybrid Particle Petri Nets (HPPN) modeling framework. The main advantage of HPPN is that they take into account knowledge-based uncertainty in the system representation and uncertainty in the diagnosis process. The HPPN-based diagnoser deals with occurrences of unobservable discrete events (such as fault events) and it is robust to false observations. It also estimates the continuous state of the system by using particle filtering. A methodology is proposed to perform model-based diagnosis on hybrid systems by using the HPPN modeling framework. The system diagnosis is computed at any time from a HPPN-based diagnoser and contains all the hypotheses over its past mode trajectory. Each hypothesis is valued with a belief degree and includes discrete and continuous state estimates, as well as the set of faults that occurred on the system up to the current time. The HPPN-based methodology is demonstrated with an application on the K11 planetary rover prototype developed by NASA Ames Research Center. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

Functional Prognostic Architecture for the Maintenance of Complex Systems

Abstract This paper addresses the problem of the maintenance of a complex system of heterogeneous components. With the decreasing costs of sensors, it now becomes possible to really benefit of on-line adaptive prognostic methods in order to support the optimization of the maintenance actions and scheduling. In this paper, we propose a generic health monitoring architecture that encompasses the available prognostic methods and provides a common support for the maintenance decision on a complex system. The output of this architecture takes into account the composed nature of the system by not only providing component prognoses but also function prognosis. In order to provide a prognosis for the whole system, component prognoses are combined by taking into account functional dependencies in the system.