Robust fault diagnosis for an exothermic semi-batch polymerization reactor under open-loop

Ertiame, Abdelkarim M.; Yu, Dingli; Feng, Yu; Gomm, J.B.

doi:10.1080/21642583.2014.984356

Cited by 8 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The KALMAN Filter (KF) can be estimated dynamical state from noisy measurements. In this method, dynamic and measurement processes can be approximated by linear Gaussian state space models [1]. This model is a practical model in engineering due to modelling of various noises.…”

Section: Introductionmentioning

confidence: 99%

Bayesian approximation for parameterized KALMAN filter for investigation and simulation of unknown noise variance trajectory following in state space models with different noise distributions

Farhad Asadi,

S. Hossein Sadati

2024

Open Access Res. J. Sci. Technol.

View full text Add to dashboard Cite

Bayesian approach can be used for parameter identification and extraction in state space models and its ability for analyzing sequence of data in dynamical system is proved in different literatures. In this paper, Bayesian approach for approximation of variances in measurement noise with KALMAN filter is applied for estimation of the dynamical state and measurement data in discrete dynamical system. Detection of uncertainty and estimation of those can be done simultaneously with adaptive KALMAN filter. This algorithm at each step time estimates noise variance and state of system with KALMAN filter. Then, approximation is formed at each step separately and at each step sufficient statistics of the state and noise variances are computed with a fixed-point iteration of a KALMAN filter. For showing influence of variance in measurement data on algorithm different simulations is applied. First, effect of variance and its distribution on detection performance is simulated in KALMAN filter without Bayesian formulation. Then simulation is applied to KALMAN filter with ability of variance tracking of measurement data.in these simulations, influence of distribution of measurement data in each step is estimated and true variance of data is obtained by algorithm and is compared in different scenarios. Afterwards, one typical modeling of nonlinear state space model with inducing noise measurement is simulated by this approach. Finally, the performance and the important limitations of this algorithm in these simulations are explained.

show abstract

Section: Introductionmentioning

confidence: 99%

Bayesian approximation for parameterized KALMAN filter for investigation and simulation of unknown noise variance trajectory following in state space models with different noise distributions

Farhad Asadi,

S. Hossein Sadati

2024

Open Access Res. J. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…So far, a variety of fault estimation schemes have been proposed in the existing literature, see e.g. [4], [6], [9], [14], [17] and the references therein. Nowadays, in response to the rapidly growing complexity of industrial systems, the time-varying nature has gradually become an indispensable means of reflecting the fast changes in system dynamics.…”

Section: Introductionmentioning

confidence: 99%

On H-infinity Estimation of Randomly Occurring Faults for A Class of Nonlinear Time-Varying Systems With Fading Channels

Dong

Wang

Ding

et al. 2016

IEEE Trans. Automat. Contr.

166

View full text Add to dashboard Cite

Abstract-This paper is concerned with the finite-horizon H∞ fault estimation problem for a class of nonlinear stochastic time-varying systems with both randomly occurring faults and fading channels. The system model (dynamical plant) is subject to Lipschitz-like nonlinearities and the faults occur in a random way governed by a set of Bernoulli distributed white sequences. The system measurements are transmitted through fading channels described by a modified stochastic Rice fading model. The purpose of the addressed problem is to design a time-varying fault estimator such that, in the presence of channel fading and randomly occurring faults, the influence from the exogenous disturbances onto the estimation errors is attenuated at the given level quantified by a H∞-norm in the mean square sense. By utilizing the stochastic analysis techniques, sufficient conditions are established to ensure that the dynamic system under consideration satisfies the prespecified performance constraint on the fault estimation, and then a recursive linear matrix inequality approach is employed to design the desired fault estimator gains. Simulation results demonstrate the effectiveness of the developed fault estimation design scheme.

show abstract

“…As such, fault estimation is further needed for the purpose of active fault tolerant control. So far, considerable research attention has been devoted to the theoretical research on the fault estimation problem, and a variety of fault estimation approaches have been developed in existing literature, see e.g., Ding, 2008;Ertiame et al, 2015;Fekih and Seelem, 2015;Wei et al, 2013;Bouibed et al, 2014;Jiang et al, 2006;Yan and Edwards, 2007) and the references therein. For example, in (Jiang et al, 2006;Xu et al, 2012;Zhang et al, 2008), the problems of fault estimation have been investigated by applying adaptive fault diagnosis observers that can improve the rapidity of fault estimation.…”

Section: Introductionmentioning

confidence: 99%

Distributed fault estimation with randomly occurring uncertainties over sensor networks

Dong

Wang

et al. 2016

International Journal of General Systems

View full text Add to dashboard Cite

This paper is concerned with the distributed fault estimation problem for a class of uncertain stochastic systems over sensor networks. The norm-bounded uncertainty enters into the system in a random way governed by a set of Bernoulli distributed white sequence. The purpose of the addressed problem is to design distributed fault estimators, via available output measurements from not only the individual sensor but also its neighboring sensors, such that the fault estimation error converges to zero exponentially in the mean square while the disturbance rejection attenuation is constrained to a give level by means of the H ∞ performance index. Intensive stochastic analysis is carried out to obtain sufficient conditions for ensuring the exponential stability as well as prescribed H ∞ performance for the overall estimation error dynamics. Simulation results are provided to demonstrate the effectiveness of the proposed fault estimation technique in this paper.

show abstract

Robust fault diagnosis for an exothermic semi-batch polymerization reactor under open-loop

Cited by 8 publications

References 13 publications

Bayesian approximation for parameterized KALMAN filter for investigation and simulation of unknown noise variance trajectory following in state space models with different noise distributions

Bayesian approximation for parameterized KALMAN filter for investigation and simulation of unknown noise variance trajectory following in state space models with different noise distributions

On H-infinity Estimation of Randomly Occurring Faults for A Class of Nonlinear Time-Varying Systems With Fading Channels

Distributed fault estimation with randomly occurring uncertainties over sensor networks

Contact Info

Product

Resources

About