A unified model-based fault diagnosis scheme for non-linear discrete-time systems with additive and multiplicative faults

Abstract

In this paper, a unified model-based fault diagnosis (MFD) scheme that deals with both multiplicative actuator and additive system faults is designed. For a class of uncertain non-linear discrete-time systems, this MFD scheme is capable of not only detecting both additive and multiplicative actuator faults but also identifying the fault type. Faults are detected by using a novel fault detection observer consisting of two online approximators in discrete-time (OLAD) and a robust adaptive term. Upon detection, a fault diagnosis scheme is introduced to determine the fault type by monitoring the input residual generated via the first OLAD output. Upon performing the diagnosis online, the appropriate OLAD is activated in the observer and the other OLAD is switched off. Thereafter, by using both the parameter update law of the active OLAD and user-selected failure threshold, an online time-to-failure scheme is introduced. In the case of multiplicative faults, boundedness of the detection residual and parameter estimation errors is shown while in the case of additive faults, the asymptotic convergence of the detection residual and parameter estimation errors is guaranteed due to the robust adaptive term. Finally, a simulation example is used to demonstrate the proposed fault diagnosis scheme.

Keywords

Diagnostics/prognosis fault detection/diagnosis learning systems neural networks non-linear systems observers

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References

Alessandri

(2003) Fault diagnosis for nonlinear systems using a bank of neural estimators. Computers in Industry 52: 271–289.

Alessandri

Parisini

(1997) Model-based fault diagnosis using nonlinear estimators: a neural approach. Proceedings of the 1997 American Control Conference, 4–6 June 1997, vol. 2, pp. 903–907.

Blake

Brown

(2007) Simultaneous, multiplicative actuator and sensor fault estimation using fuzzy observers. Fuzzy Systems Conference, FUZZ-IEEE 2007, IEEE International, 23–26 July 2007, pp. 1–6.

Caccavale

Villani

(2004) An adaptive observer for fault diagnosis in nonlinear discrete-time systems. Proceedings of the 2004 American Control Conference, 30 June–2 July 2004, vol. 3, pp. 2463–2468.

Chee Pin

Edwards

(2004) Multiplicative fault reconstruction using sliding mode observers. 5th Asian Control Conference, 20–23 July 2004, Vol. 2, pp. 957–962.

Chen

Patton

(1999) Robust Model-based Fault Diagnosis for Dynamic Systems. Dordrecht: Kluwer Academic Publishers.

Dash

Venkatasubramanian

(2000) Challenges in the industrial applications of fault diagnostic systems. Computers & Chemical Engineering 24: 785–791.

Demetriou

Polycarpou

(1998) Incipient fault diagnosis of dynamical systems using online approximators. IEEE Transactions on Automatic Control 43: 1612–1617.

Edwards

Spurgeon

Patton

(2000) Sliding mode observers for fault detection and isolation. Automatica 36: 541–553.

10.

Frank

(1990) Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: a survey and some new results. Automatica 26: 459–474.

11.

Gertler

(1988) Survey of model-based failure detection and isolation in complex plants. IEEE Control Systems Magazine 8: 3–11.

12.

Huang

H-P

C-C

Jeng

J-C

(2007) Multiple multiplicative fault diagnosis for dynamic processes via parameter similarity measures. Industrial & Engineering Chemistry Research 46: 4517–4530.

13.

Isermann

(2005) Model-based fault-detection and diagnosis – status and applications. Annual Reviews in Control 29: 71–85.

14.

Jagannathan

(2006) Neural Network Control of Nonlinear Discrete –time Systems. New York: CRC Publications.

15.

Patton

Chen

Lopez-Toribio

(1998) Fuzzy observers for nonlinear dynamic systems fault diagnosis. Proceedings of the 37th IEEE Conference on Decision and Control, vol. 1, pp. 84–89.

16.

Polycarpou

Helmicki

(1995) Automated fault detection and accommodation: a learning systems approach. IEEE Transactions on Systems, Man and Cybernetics 25: 1447–1458.

17.

Talebi

Khorasani

(2007) Neural network-based actuator gain fault detection and isolation strategy for nonlinear systems. 46th IEEE Conference on Decision and Control, 12–14 December 2007, pp. 2614–2619.

18.

Talebi

Khorasani

Tafazoli

(2009) A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite’s attitude control subsystem. IEEE Transactions on Neural Networks 20: 45–60.

19.

Thumati

Jagannathan

(2010) A model-based fault-detection and prediction scheme for nonlinear multivariable discrete-time systems with asymptotic stability guarantees. IEEE Transactions on Neural Networks 21: 404–423.

20.

Xiaodong

Polycarpou

Parisini

(2002) A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems. IEEE Transactions on Automatic Control 47: 576–593.

21.

Zhang

Morris

(1994) On-line process fault diagnosis using fuzzy neural networks. Intelligent Systems Engineering 3: 37–47.