Dynamic Fault Diagnosis for Semi-Batch Reactor under Closed-Loop Control via Independent Radial Basis Function Neural Network
In this paper, a robust fault detection and isolation
(FDI) scheme is developed to monitor a multivariable nonlinear
chemical process called the Chylla-Haase polymerization reactor,
when it is under the cascade PI control. The scheme employs a radial
basis function neural network (RBFNN) in an independent mode to
model the process dynamics, and using the weighted sum-squared
prediction error as the residual. The Recursive Orthogonal Least
Squares algorithm (ROLS) is employed to train the model to
overcome the training difficulty of the independent mode of the
network. Then, another RBFNN is used as a fault classifier to isolate
faults from different features involved in the residual vector. Several
actuator and sensor faults are simulated in a nonlinear simulation of
the reactor in Simulink. The scheme is used to detect and isolate the
faults on-line. The simulation results show the effectiveness of the
scheme even the process is subjected to disturbances and
uncertainties including significant changes in the monomer feed rate,
fouling factor, impurity factor, ambient temperature, and
measurement noise. The simulation results are presented to illustrate
the effectiveness and robustness of the proposed method.
[1] Barton, R. S. and Himmelblau, D. M. (1997) Online prediction of
polymer product quality in an industrial reactor using recurrent neural
networks, 114 vol.1.
[2] Beyer, M.-A., Grote, W. and Reinig, G. (2008) 'Adaptive exact
linearization control of batch polymerization reactors using a Sigma-
Point Kalman Filter', Journal of Process Control, 18(7–8), 663-675.
[3] Chen, S., Billings, S. A., Cowan, C. F. N. and Grant, P. M. (1990) 'Nonlinear
systems identification using radial basis functions', International
Journal of Systems Science, 21(12), 2513-2539.
[4] Chylla, R. W. and Haase, D. R. (1993) 'Temperature control of
semibatch polymerization reactors', Computers & Chemical
Engineering, 17(3), 257-264.
[5] Deibert, R. and Isermann, R. (1992) 'Examples for fault detection in
closed loops', Annual Review in Automatic Programming, 17(0), 235-
240.
[6] Ertiame, A. M., Dingli, Y., Feng, Y. and Gomm, J. B. (2013) Fault
detection and isolation for open-loop Chylla-Haase polymerization
reactor, 1-6.
[7] Ertiame, A. M., et al. (2014). "Robust fault diagnosis for an exothermic
semi-batch polymerization reactor under open-loop." Systems Science
& Control Engineering 3(1): 14-23.
[8] Fabrizio Caccavale, Mario Iamarino, Francesco Pierri and Tufano, V.
(2011) Control and Monitoring of Chemical Batch Reactors, London:
Springer-Verlag London Limited.
[9] Ferrari, R. M. G., Parisini, T. and Polycarpou, M. M. (2008) A robust
fault detection and isolation scheme for a class of uncertain input-output
discrete-time nonlinear systems, 2804-2809.
[10] Frank, P. M. and Köppen-Seliger, B. (1997) 'Fuzzy logic and neural
network applications to fault diagnosis', International Journal of
Approximate Reasoning, 16(1), 67-88.
[11] Gertler, J. J. (1988) 'Survey of model-based failure detection and
isolation in complex plants', Control Systems Magazine, IEEE, 8(6), 3-
11. [12] Gomm, J. B., et al. (1996). "Enhancing the non-linear modelling
capabilities of MLP neural networks using spread encoding." Fuzzy Sets
and Systems 79(1): 113-126.
[13] Gomm, J. B. and Yu, D. L. (2000) 'Selecting radial basis function
network centers with recursive orthogonal least squares training', Neural
Networks, IEEE Transactions on, 11(2), 306-314.
[14] Graichen, K., Hagenmeyer, V. and Zeitz, M. (2005) Adaptive
Feedforward Control with Parameter Estimation for the Chylla-Haase
Polymerization Reactor, 3049-3054.
[15] Graichen, K., Hagenmeyer, V. and Zeitz, M. (2006) 'Feedforward
control with online parameter estimation applied to the Chylla–Haase
reactor benchmark', Journal of Process Control, 16(7), 733-745.
[16] Isermann, R. (1984) 'Process fault detection based on modeling and
estimation methods—A survey', Automatica, 20(4), 387-404.
[17] Patton, R. J. and Chen, J. (1992) Robustness in quantitative model-based
fault diagnosis, 4/1-417.
[18] Patton, R. J., Chen, J. and Siew, T. M. (1994) Fault diagnosis in
nonlinear dynamic systems via neural networks, 1346-1351 vol.2.
[19] Pierri, F., Paviglianiti, G., Caccavale, F. and Mattei, M. (2008)
'Observer-based sensor fault detection and isolation for chemical batch
reactors', Engineering Applications of Artificial Intelligence, 21(8),
1204-1216.
[20] Polycarpou, M. M. and Helmicki, A. J. (1995) 'Automated fault
detection and accommodation: a learning systems approach', Systems,
Man and Cybernetics, IEEE Transactions on, 25(11), 1447-1458.
[21] Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N. and Yin, K.
(2003) 'A review of process fault detection and diagnosis: Part III:
Process history based methods', Computers & Chemical
Engineering, 27(3), 327-346.
[22] Wang, S. W., Yu, D. L., Gomm, J. B., Page, G. F. and Douglas, S. S.
(2006) 'Adaptive neural network model based predictive control for air–
fuel ratio of SI engines', Engineering Applications of Artificial
Intelligence, 19(2), 189-200.
[23] Xiaodong, Z. (2011) 'Sensor Bias Fault Detection and Isolation in a
Class of Nonlinear Uncertain Systems Using Adaptive Estimation',
Automatic Control, IEEE Transactions on, 56(5), 1220-1226.
[24] Xiaodong, Z., Polycarpou, M. and Parisini, T. (2001) Fault isolation in a
class of nonlinear uncertain input-output systems, 1741-1746 vol.2.
[25] Xiaodong, Z., Polycarpou, M. M. and Parisini, T. (2002) 'A robust
detection and isolation scheme for abrupt and incipient faults in
nonlinear systems', Automatic Control, IEEE Transactions on, 47(4),
576-593.
[26] Yu, D. L., Gomm, J. B. and Williams, D. (1999) 'Sensor fault diagnosis
in a chemical process via RBF neural networks', Control Engineering
Practice, 7(1), 49-55.
[1] Barton, R. S. and Himmelblau, D. M. (1997) Online prediction of
polymer product quality in an industrial reactor using recurrent neural
networks, 114 vol.1.
[2] Beyer, M.-A., Grote, W. and Reinig, G. (2008) 'Adaptive exact
linearization control of batch polymerization reactors using a Sigma-
Point Kalman Filter', Journal of Process Control, 18(7–8), 663-675.
[3] Chen, S., Billings, S. A., Cowan, C. F. N. and Grant, P. M. (1990) 'Nonlinear
systems identification using radial basis functions', International
Journal of Systems Science, 21(12), 2513-2539.
[4] Chylla, R. W. and Haase, D. R. (1993) 'Temperature control of
semibatch polymerization reactors', Computers & Chemical
Engineering, 17(3), 257-264.
[5] Deibert, R. and Isermann, R. (1992) 'Examples for fault detection in
closed loops', Annual Review in Automatic Programming, 17(0), 235-
240.
[6] Ertiame, A. M., Dingli, Y., Feng, Y. and Gomm, J. B. (2013) Fault
detection and isolation for open-loop Chylla-Haase polymerization
reactor, 1-6.
[7] Ertiame, A. M., et al. (2014). "Robust fault diagnosis for an exothermic
semi-batch polymerization reactor under open-loop." Systems Science
& Control Engineering 3(1): 14-23.
[8] Fabrizio Caccavale, Mario Iamarino, Francesco Pierri and Tufano, V.
(2011) Control and Monitoring of Chemical Batch Reactors, London:
Springer-Verlag London Limited.
[9] Ferrari, R. M. G., Parisini, T. and Polycarpou, M. M. (2008) A robust
fault detection and isolation scheme for a class of uncertain input-output
discrete-time nonlinear systems, 2804-2809.
[10] Frank, P. M. and Köppen-Seliger, B. (1997) 'Fuzzy logic and neural
network applications to fault diagnosis', International Journal of
Approximate Reasoning, 16(1), 67-88.
[11] Gertler, J. J. (1988) 'Survey of model-based failure detection and
isolation in complex plants', Control Systems Magazine, IEEE, 8(6), 3-
11. [12] Gomm, J. B., et al. (1996). "Enhancing the non-linear modelling
capabilities of MLP neural networks using spread encoding." Fuzzy Sets
and Systems 79(1): 113-126.
[13] Gomm, J. B. and Yu, D. L. (2000) 'Selecting radial basis function
network centers with recursive orthogonal least squares training', Neural
Networks, IEEE Transactions on, 11(2), 306-314.
[14] Graichen, K., Hagenmeyer, V. and Zeitz, M. (2005) Adaptive
Feedforward Control with Parameter Estimation for the Chylla-Haase
Polymerization Reactor, 3049-3054.
[15] Graichen, K., Hagenmeyer, V. and Zeitz, M. (2006) 'Feedforward
control with online parameter estimation applied to the Chylla–Haase
reactor benchmark', Journal of Process Control, 16(7), 733-745.
[16] Isermann, R. (1984) 'Process fault detection based on modeling and
estimation methods—A survey', Automatica, 20(4), 387-404.
[17] Patton, R. J. and Chen, J. (1992) Robustness in quantitative model-based
fault diagnosis, 4/1-417.
[18] Patton, R. J., Chen, J. and Siew, T. M. (1994) Fault diagnosis in
nonlinear dynamic systems via neural networks, 1346-1351 vol.2.
[19] Pierri, F., Paviglianiti, G., Caccavale, F. and Mattei, M. (2008)
'Observer-based sensor fault detection and isolation for chemical batch
reactors', Engineering Applications of Artificial Intelligence, 21(8),
1204-1216.
[20] Polycarpou, M. M. and Helmicki, A. J. (1995) 'Automated fault
detection and accommodation: a learning systems approach', Systems,
Man and Cybernetics, IEEE Transactions on, 25(11), 1447-1458.
[21] Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N. and Yin, K.
(2003) 'A review of process fault detection and diagnosis: Part III:
Process history based methods', Computers & Chemical
Engineering, 27(3), 327-346.
[22] Wang, S. W., Yu, D. L., Gomm, J. B., Page, G. F. and Douglas, S. S.
(2006) 'Adaptive neural network model based predictive control for air–
fuel ratio of SI engines', Engineering Applications of Artificial
Intelligence, 19(2), 189-200.
[23] Xiaodong, Z. (2011) 'Sensor Bias Fault Detection and Isolation in a
Class of Nonlinear Uncertain Systems Using Adaptive Estimation',
Automatic Control, IEEE Transactions on, 56(5), 1220-1226.
[24] Xiaodong, Z., Polycarpou, M. and Parisini, T. (2001) Fault isolation in a
class of nonlinear uncertain input-output systems, 1741-1746 vol.2.
[25] Xiaodong, Z., Polycarpou, M. M. and Parisini, T. (2002) 'A robust
detection and isolation scheme for abrupt and incipient faults in
nonlinear systems', Automatic Control, IEEE Transactions on, 47(4),
576-593.
[26] Yu, D. L., Gomm, J. B. and Williams, D. (1999) 'Sensor fault diagnosis
in a chemical process via RBF neural networks', Control Engineering
Practice, 7(1), 49-55.
@article{"International Journal of Electrical, Electronic and Communication Sciences:71554", author = "Abdelkarim M. Ertiame and D. W. Yu and D. L. Yu and J. B. Gomm", title = "Dynamic Fault Diagnosis for Semi-Batch Reactor under Closed-Loop Control via Independent Radial Basis Function Neural Network", abstract = "In this paper, a robust fault detection and isolation
(FDI) scheme is developed to monitor a multivariable nonlinear
chemical process called the Chylla-Haase polymerization reactor,
when it is under the cascade PI control. The scheme employs a radial
basis function neural network (RBFNN) in an independent mode to
model the process dynamics, and using the weighted sum-squared
prediction error as the residual. The Recursive Orthogonal Least
Squares algorithm (ROLS) is employed to train the model to
overcome the training difficulty of the independent mode of the
network. Then, another RBFNN is used as a fault classifier to isolate
faults from different features involved in the residual vector. Several
actuator and sensor faults are simulated in a nonlinear simulation of
the reactor in Simulink. The scheme is used to detect and isolate the
faults on-line. The simulation results show the effectiveness of the
scheme even the process is subjected to disturbances and
uncertainties including significant changes in the monomer feed rate,
fouling factor, impurity factor, ambient temperature, and
measurement noise. The simulation results are presented to illustrate
the effectiveness and robustness of the proposed method.", keywords = "Robust fault detection, cascade control, independent
RBF model, RBF neural networks, Chylla-Haase reactor, FDI under
closed-loop control.", volume = "9", number = "12", pages = "1402-12", }
