Event scheduling of timed automata using Dioid Algebra

Baniardalani, Sobhi; Askari, Javad; Afzalian, Ali A.

doi:10.1109/icca.2010.5524169

Cited by 1 publication

(3 citation statements)

References 15 publications

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“…For the first case, Ĳ ij is extracted directly from the transition matrix T. whereas in the latter case, i T must be predicted by means of computing event scheduling table of DG. An algorithm which uses Dioid Algebra is presented in [14].…”

Section: Ta Based Diagnosermentioning

confidence: 99%

“…Hence, an algorithm is needed to extract and predict the features. We use the algorithm presented in [14].To do this, firstly the state transition matrix of TA is determined in a Dioid Algebra similar to (17). Then as an intermediate step, Event Scheduling Table (EST) for all events in the k th step is computed.…”

Section: Frc42mentioning

confidence: 99%

“…Then as an intermediate step, Event Scheduling Table (EST) for all events in the k th step is computed. As it is shown in [14], this computation can be carried out recursively and in a linear fashion in Diode Algebra. At the third step, timed-event execution paths of all events up to the k th event occurrence are determined by sorting this EST in respect to time.…”

Section: Frc42mentioning

confidence: 99%

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A novel analytical framework for qualitative Model-Based Fault Diagnosis

Baniardalani

Askari

Afzalian³

2010

Ieee Icca 2010

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This paper presents a unified analytical framework for qualitative Model-Based Fault Diagnosis (MBFD), similar to the quantitative MBFD. Dioid Algebra is used in addition to ordinary Algebra for simulation qualitative models. The framework is illustrated and adapted in details for three main qualitative diagnostic methods which employ Stochastic, Non-Deterministic, and Timed Automata, respectively. Using the proposed methodology, we are able to compute quantitative residuals for qualitative models. Therefore some useful and practical computational tasks can be carried out on the obtained residuals. One of the main contributions of the paper is introducing a new approach to qualitative structured residual generation, which is applied to timed automata models. I. INTRODUCTION rocess diagnosis is the task of detecting and isolating faults in technical processes from input and output data. A Model-Based Fault Diagnosis (MBFD) system uses an explicit model of the dynamical systems under investigation. This model incorporates the knowledge about the faultless and faulty system behavior [1-3]. There are two basic MBFD approaches considering the model and the measurement information.Quantitative MBFD The models are represented by differential or difference equations, describing the dependencies among the signals quantitatively. The signals are measured numerically [1,3]. An analytical framework was presented for quantitative MBFD in [1], and the main diagnostic methods were classified in parameter estimation, observer based and parity equations methods. Qualitative MBFDIn these approaches, the values of signals are qualitative such as intervals or symbols, and the qualitative model describes the relation among qualitative signals [3][4][5].Quantitative MBFD approaches have been developed in the last 20 years [1-3], especially for linear systems. A great majority of these methods aren't generally effective for solving diagnostic problems in neither Hybrid Systems (HS) nor nonlinear and uncertain parameter systems [1,6].

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Section: Ta Based Diagnosermentioning

confidence: 99%

Section: Frc42mentioning

confidence: 99%

Section: Frc42mentioning

confidence: 99%

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