Monitoring of dynamic processes by rectangular hybrid automata

Karoui, Mohamed Fathi; Alla, Hassane; Chatti, Abderrazak

doi:10.1016/j.nahs.2010.05.004

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…Dynamic PCA Linear PCA [4,8,78,79] Partial PCA [12,13] Kernel PCA [80] Multiscale [81] Dynamic ICA ICA [14,[82][83][84][85][86][87] Kernel ICA [88] Slow feature analysis [22][23][24] ICA Standard [89] Kernel [88] Phase space and related methods x k and y k are the respective state and measurement vectors of the system, and w k and v k are the plant disturbances and measurement errors, respectively, at time k. State space models and their variants have been considered by several authors [65][66][67][68][69][70][71][72][73][74][75].…”

Section: Unsupervised Feature Extractionmentioning

confidence: 99%

Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series

Aldrich¹

2019

Time Series Analysis - Data, Methods, and Applications

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Fault diagnosis in continuous dynamic systems can be challenging, since the variables in these systems are typically characterized by autocorrelation, as well as time variant parameters, such as mean vectors, covariance matrices, and higher order statistics, which are not handled well by methods designed for steady state systems. In dynamic systems, steady state approaches are extended to deal with these problems, essentially through feature extraction designed to capture the process dynamics from the time series. In this chapter, recent advances in feature extraction from signals or multivariate time series are reviewed. These methods can subsequently be considered in a classical statistical monitoring framework, such as used for steady state systems. In addition, an extension of nonlinear signal processing based on the use of unthresholded or global recurrence quantification analysis is discussed, where two multivariate image methods based on gray level co-occurrence matrices and local binary patterns are used to extract features from time series. When considering the well-known simulated Tennessee Eastman process in chemical engineering, it is shown that time series features obtained with this approach can be an effective means of discriminating between different fault conditions in the system. The approach provides a general framework that can be extended in multiple ways to time series analysis.

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Section: Unsupervised Feature Extractionmentioning

confidence: 99%

Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series

Aldrich¹

2019

Time Series Analysis - Data, Methods, and Applications

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“…Ladder logic is the programming language that is primarily used to develop software for PLCs. It represents a program by a graphical diagram based on the circuit diagrams of relay logic hardware [25,28].…”

Section: Objectives Treementioning

confidence: 99%

Application of System Analysis in Order to Monitor Grain Silos

LAKHOUA¹

2019

JESR

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After a presentation of the management of grain silos, we present the necessity of the system analysis and the identification of information in grain silos based on the OOPP method (Objectives Oriented Project Planning). This kind of analysis enables an efficient management and monitoring of the activities of grain silos. In fact, the OOPP method constitutes a tool of a global system modeling that allows us to analyze a difficult situation by a hierarchically decomposition until attainment a basic level allowing to an operational planning. Then, the aim of this paper is the application of the OOPP method to the analysis of grain silos which enables us to contribute to the development and deployment of an Information System and monitoring of grain storage.

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“…A Rectangular hybrid automaton is a 7tuple (Q, X, Σ, E, inv, flux, init, M) [10] where: • Σ is a whole of events, As mentioned above in this paper, generally the study of a system modelled by a rectangular hybrid automaton is based on the reachability analysis of the automatons states. To know if a region R is reachable since a region R 0 , two methods can be used; the forward analysis and the backward analysis [11]. A region (or a symbolic state) of an RHA is represented by a pair 〈q, z〉, where q corresponds to a location of automaton and z a region of the space of continuous state, represented by a polyhedron.…”

Section: Definitionmentioning

confidence: 99%

Monitoring of dynamic process by hybrid automata

Karoui

Alla²,

Chatti

2011

Eighth International Multi-Conference on Systems, Signals &Amp; Devices

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In this paper we are going to present a monitoring approach of dynamic systems by using the hybrid automaton. At first we shall use the linear hybrid automatons, in continuation we are going to enrich our monitoring method by using the rectangular hybrid automatons. This work is based on the observation of the dynamic evolution of these systems, and activates an alarm if there is any infringement of the constraints which theirs are applied. The monitoring system, we propose, makes it possible to detect this infringement as soon as possible thanks to the reachability analysis. Our monitoring method will be applied to real physical systems.

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Monitoring of dynamic processes by rectangular hybrid automata

Cited by 7 publications

References 9 publications

Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series

Process Fault Diagnosis for Continuous Dynamic Systems Over Multivariate Time Series

Application of System Analysis in Order to Monitor Grain Silos

Monitoring of dynamic process by hybrid automata

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