Process fault detection based on modeling and estimation methods—A survey

Isermann, Rolf

doi:10.1016/0005-1098(84)90098-0

Cited by 2,098 publications

(560 citation statements)

References 8 publications

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“…§ The assumed measurement variances are incorrect resulting in a poorly tuned estimation algorithm. § Since detection methods must be sensitive to the occurrence of faults but robust to noises, modeling errors and signal variations, the following trade-off exist [Isermann, 1984] …”

Section: Problem Discussionmentioning

confidence: 99%

“…The second is herein referred to as the "balance-based detection method" and it uses conservation principles for testing the consistency of the variables measured. Isermann [1984] and Frank [1990] offer survey of the residual -based detection method. Alcorta García and Frank [1997] reviewed observer based approaches to several classes of deterministic nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

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Measurement processing for state estimation and fault identification in batch fermentations

Dondo

2004

Braz. J. Chem. Eng.

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-This work describes an application of maximum likelihood identification and statistical detection techniques for determining the presence and nature of abnormal behaviors in batch fermentations. By appropriately organizing these established techniques, a novel algorithm that is able to detect and isolate faults in nonlinear and uncertain processes was developed. The technique processes residuals from a nonlinear filter based on the assumed model of fermentation. This information is combined with mass balances to conduct statistical tests that are used as the core of the detection procedure. The approach uses a sliding window to capture the present statistical properties of filtering and mass-balance residuals. In order to avoid divergence of the nonlinear monitor filter, the maximum likelihood states and parameters are periodically estimated. The maximum likelihood parameters are used to update the kinetic parameter values of the monitor filter. If the occurrence of a fault is detected, alternative faulty model structures are evaluated statistically through the use of log-likelihood function values and χ 2 detection tests. Simulation obtained for xanthan gum batch fermentations are encouraging.

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Section: Problem Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement processing for state estimation and fault identification in batch fermentations

Dondo

2004

Braz. J. Chem. Eng.

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“…Eine ausführlicheÜbersicht zu identifikationsbasierten Methoden findet sich unter anderem in [110,111,204]. Grundgedanke ist, dass sich auftretende Fehler im Ein-/Ausgangsverhalten eines Systems niederschlagen.…”

Section: Identifikationsbasierte Ansätze Zur Fehlerdiagnoseunclassified

Entwurf robuster modellbasierter Fehlerisolationsfilter

Wahrburg

2014

At - Automatisierungstechnik

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“…Fault detection and isolation (FDI) techniques have attracted much interest due to the increasing demand for good performance and higher standards of safety and reliability of technical plants for improving the supervision and monitoring as part of the overall control of processes [1]. FDI has become a critical issue in the operation of highperformance chemical plants, nuclear plants, airplanes, ships, submarines, and space vehicles, etc.…”

Section: Introductionmentioning

confidence: 99%

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

Ertiame

Feng

et al. 2014

Systems Science & Control Engineering

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Abstract-Fault detection and fault diagnosis have become increasingly important for improvement of the reliability, safety and efficiency of many technical processes. In this research, a new robust fault detection and isolation (FDI) scheme is developed for open-loop Chylla-Haase polymerization reactor. This reactor has been widely used as an industrial Benchmark. The independent Radial Basis Function (RBF) Neural Network (RBFNN) is employed here for on-line diagnosis of faults on the actuator, sensors, and reactor components when the system is subjected to system uncertainties and disturbances. Two different techniques to employ RBF neural networks are investigated. Firstly, an independent neural network is used to model the reactor dynamics and generate residuals. Secondly, an additional RBF neural network is developed as a classifier to isolate faults from the generated residuals. Three sensor faults and one actuator fault are simulated on the Chylla-Haase reactor. Moreover, many practical disturbances and system uncertainties, such as monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise are modelled. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

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Process fault detection based on modeling and estimation methods—A survey

Cited by 2,098 publications

References 8 publications

Measurement processing for state estimation and fault identification in batch fermentations

Measurement processing for state estimation and fault identification in batch fermentations

Entwurf robuster modellbasierter Fehlerisolationsfilter

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

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