Sensor fault detection and isolation via high-gain observers: Application to a double-pipe heat exchanger

Escobar, R.F.; Astorga‐Zaragoza, C.M.; Téllez-Anguiano, Adriana del Carmen; Juárez-Romero, D.; Hernández, J.A.; Guerrero-Ramírez, G.V.

doi:10.1016/j.isatra.2011.03.002

Cited by 48 publications

(27 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The obtained results show an acceptable performance of the RBF neural network for estimating the nonmeasurable states. Note that none of the MSE values exceeds the thresholds proposed by [43], 1 × 10 −3 . Figure 1 shows the proposed closed-loop architecture.…”

Section: Theorem 3 the Model Of The Fed-batch Bioreactor (See (A1) mentioning

confidence: 93%

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

et al. 2017

View full text Add to dashboard Cite

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

show abstract

Section: Theorem 3 the Model Of The Fed-batch Bioreactor (See (A1) mentioning

confidence: 93%

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Many authors working in the field of process control and controllability prefer the constant parameter because of the computational simplicity, and a simplified dynamic model containing only one cell is often the case on application of fault detection and isolation, like sensor and/or actuator fault detection and isolation methods proposed in [19][20][21][22]. However, it is widely accepted that fouling influences the dynamics of overall heat transfer coefficient; thus constant value leads to some mismatch between the model and physical process, and this mismatch is usually handled as unstructured model uncertainties, like in [19]. In order to better minimize the mismatch, fouling influence was developed by considering heat transfer coefficient is slowly decreasing.…”

Section: Introductionmentioning

confidence: 99%

“…To compute fouling, online updating rules based on observers are widely investigated, like extended Kalman filter (EKF) in [23], adaptive high gain observer in [24], and recursive least-squares method in [17]. Another popular method is to calculate the parameter offline, as proposed in [19]. Several fault diagnosis (FD) approaches have been proposed with parameter regularly updated; for this purpose, H∞ approach in [25], adaptive observer in [26,27], polynomial fuzzy observer in [28], and EKF in [29] are mostly used.…”

Section: Introductionmentioning

confidence: 99%

An Integrated FDD Approach for an Intensified HEX/Reactor

Zhang

Cabassud

et al. 2018

Journal of Control Science and Engineering

View full text Add to dashboard Cite

In this paper, a fault detection and diagnosis (FDD) scheme is developed for a class of intensified HEX/reactor, in which faults caused by sensor, actuator, and process are taken into account in the unified framework. By considering overall heat transfer coefficient as a function of fouling and fluid flow rate, a dynamic model which is capable of identifying these two faults simultaneously is derived. Sensor measurements, together with estimation by adaptive high gain observers, are processed, aimed at identifying sensor faults and providing adequate estimation to substitute faulty measurements. Then reliable measurements are fed to several banks of interval filters to generate several banks of residuals; each bank of residuals is sensitive to a particular process parameter/actuator. By evaluating these residuals, process/actuator fault isolation and identification are achieved. The proposed strategy is applied to actual data retrieved from a new intensified heat exchanger reactor. Simulation results confirm the applicability and robustness of the proposed methodology.

show abstract

“…Several techniques inspired from control theory [4][5][6], such as adaptive observers [7][8][9][10], reference models [11][12][13], and extended Kalman filter [14]. Whatever the chosen method is, the sensor design procedure can be summarized by Fig.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of soft sensor design for real-time speed and position estimation of PMSM

Omrane¹,

Etien²,

Dib³

et al. 2015

ISA Transactions

View full text Add to dashboard Cite

Sensor fault detection and isolation via high-gain observers: Application to a double-pipe heat exchanger

Cited by 48 publications

References 20 publications

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

An Integrated FDD Approach for an Intensified HEX/Reactor

Modeling and simulation of soft sensor design for real-time speed and position estimation of PMSM

Contact Info

Product

Resources

About