State Recognition in Discrete Dynamical Systems using Petri Nets and Evidence Theory

Rombaut, Michèle; Jarkass, Iman; Denœux, Thierry

doi:10.1007/3-540-48747-6_32

Cited by 12 publications

(11 citation statements)

References 4 publications

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“…One of the first tools used for the analysis of temporal data in the TBM was proposed in 1999 by Rombaut et al [19] but this tool is not robust to noise and no classification criterion was proposed. The generalized HMM [20] proposed in 2000 is narrowed down to possibility measures and thus is not able to manage belief functions.…”

Section: Tbm For State Sequence Recognitionmentioning

confidence: 99%

Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions

Ramasso

Gouriveau

2010

2010 Prognostics and System Health Management Conference

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Condition-based maintenance is nowadays considered as a key-process in maintenance strategies and prognostics appears to be a very promising activity as it should permit to not engage inopportune spending. Various approaches have been developed and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets since a lot of methods rely on probability theory and/or on artificial neural networks. This step is thus time-consuming and generally made in batch mode which can be restrictive in practical application when few data are available. A method for prognostics is proposed to face up this problem of lack of information and missing prior knowledge. The approach is based on the integration of three complementary modules and aims at predicting the failure mode early while the system can switch between several functioning modes. The three modules are: 1) observation selection based on information theory and Choquet Integral, 2) prediction relying on an evolving real-time neuro-fuzzy system and 3) classification into one of the possible functioning modes using an evidential Markovian classifier based on Dempster-Shafer theory. Experiments concern the prediction of an engine health based on more than twenty observations.

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Section: Tbm For State Sequence Recognitionmentioning

confidence: 99%

Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions

Ramasso

Gouriveau

2010

2010 Prognostics and System Health Management Conference

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“…However, either the prior or the belief state is evidential but not both, thereby underlying probability assumptions are present. Rombaut et al [9] proposed a generalization of a Petri Net to belief functions based on the Generalized Bayesian Theorem (GBT) [8]. However, it is not robust to noise because links between states at successive instants are given by an evolving and sparse transition matrix depending on sensors measures.…”

Section: Introductionmentioning

confidence: 99%

Contribution of belief functions to hidden markov models with an application to fault diagnosis

Ramasso¹

2009

2009 IEEE International Workshop on Machine Learning for Signal Processing

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“…To our knowledge, the problem of state sequence recognition based on belief functions was first stated by Rombaut et al [29] in 1999. They used a simple singly-connected tree to represent a sequence of states.…”

Section: A Synthesis Of Methods For Dynamical Systems Analysis Bamentioning

confidence: 99%

Time-Sliced Temporal Evidential Networks: The case of Evidential HMM with application to dynamical system analysis

Serir

Ramasso

Zerhouni

2011

2011 IEEE Conference on Prognostics and Health Management

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Abstract-Diagnostics and prognostics of health states are important activities in the maintenance process strategy of dynamical systems. Many approaches have been developed for this purpose and we particularly focus on data-driven methods which are increasingly applied due to the availability of various cheap sensors. Most data-driven methods proposed in the literature rely on probability density estimation. However, when the training data are limited, the estimated parameters are no longer reliable. This is particularly true for data in faulty states which are generally expensive and difficult to obtain. In order to solve this problem, we propose to use the theory of belief functions as described by Dempster, Shafer (Theory of Evidence) and Smets (Transferable Belief Model). A few methods based on belief functions have been proposed for diagnostics and prognostics of dynamical systems. Among these methods, Evidential Hidden Markov Models (EvHMM) seems promising and extends usual HMM to belief functions. Inference tools in EvHMM have already been developed, but parameter training has not fully been considered until now or only with strong assumptions. In this paper, we propose to complete the generalization of HMM to belief functions with a method for automatic parameter training. The generalization of this training procedure to more general Time-Sliced Temporal Evidential Network (TSTEN) is discussed paving the way for a further generalization of Dynamic Bayesian Network to belief functions with potential applications to diagnostics and prognostics. An application to time series classification is proposed.

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State Recognition in Discrete Dynamical Systems using Petri Nets and Evidence Theory

Cited by 12 publications

References 4 publications

Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions

Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions

Contribution of belief functions to hidden markov models with an application to fault diagnosis

Time-Sliced Temporal Evidential Networks: The case of Evidential HMM with application to dynamical system analysis

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