A novel model-based algorithm for fault detection in stochastic linear and non-linear systems is proposed. The non-linear minimum variance estimation technique is used to generate a residual signal, which is then used to detect actuator and sensor faults in the system. The main advantage of the approach is the simplicity of the non-linear estimator theory and the straightforward structure of the resulting solution. Simulation examples are presented to illustrate the design procedure and the type of results obtained. The results demonstrate that both actuator and sensor faults can be detected successfully.
AlkayaAEkerİ (2011) Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application. ISA Transactions50: 287–302.
2.
AlrowaieFGopaluniRBKwokKE (2012) Fault detection and isolation in stochastic non-linear state-space models using particle filters. Control Engineering Practice20: 1016–1032.
ChowEYWillskyAS (1984) Analytical redundancy and the design of robust failure detection systems. IEEE Transactions on Automatic Control29(7): 603–614.
5.
ChowEYWillskyAS (1988) Analytical redundancy and the design of robust failure detection systems. IEEE Transactions on Automatic Control29: 603–614.
6.
FerdowsiHJagannathanS (2013) A unified model-based fault diagnosis scheme for nonlinear discrete-time systems with additive and multiplicative faults. Transactions of the Institute of Measurement and Control35(6): 452–462.
7.
FrankPM (1987) Advanced fault detection and isolation schemes using non-linear and robust observers (survey paper). Proceedings of the 10th IFAC World Congress Munich, pp. 63–68.
8.
FrankPM (1990) Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy. Automatica26: 459–474.
9.
FrankPM (1994) Enhancement of robustness in observer-based fault detection. International Journal of Control59(4): 955–981.
10.
FrankPMDingX (1994) Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis. Automatica30(4): 789–804.
11.
GerasimosGR (2012) A derivative-free Kalman filtering approach to state estimation-based control of nonlinear systems. IEEE Transactions on Industrial Electronics59(10): 3987–3997.
12.
GertlerJ (1998) Fault Detection and Diagnosis in Engineering Systems. New York: Marcel Dekker.
13.
GrimbleMJ (1995) Time-varying polynomial systems approach to multichannel optimal linear filtering. International Conference on Acoustics, Speech and Signal Processing, Detroit, MI, 9–12 May, vol. 2, pp. 1500–1503.
GrimbleMJ (2006) Robust Industrial Control Systems: Optimal Design Approach for Polynomial Systems. Chichester: John Wiley.
16.
GrimbleMJ (2007) NMV optimal estimation for nonlinear discrete-time multi-channel systems. 46th IEEE Conference on Decision and Control, New Orleans, LA, 12–14 December, pp. 4281–4286.
17.
GrimbleMJ (2011) Nonlinear minimum variance state based estimation for discrete-time multi-channel systems. IET Journal on Signal Processing5(4): 365–378.
18.
GrimbleMJ (2012) Nonlinear minimum variance estimation for state-dependent discrete-time systems. IET Journal on Signal Processing6(4): 379–391. doi: 10.1049/iet-spr.2010.0220.
19.
GrimbleMJShamsherAN (2010) Optimal minimum variance estimation for non-linear discrete-time multichannel systems. IET Journal on Signal Processing4(6): 618–629.
20.
HurSHKatebiRTaylorA (2011) Model-based fault monitoring of a plastic film extrusion process. IET Control Theory & Applications5(18): 2075–2088.
21.
IsermannR (2006) Fault-diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Berlin: Springer.
22.
KuceraV (1979) Discrete Linear Control. Chichester: Wiley.
23.
KuceraV (1980) Stochastic multivariable control: a polynomial equation approach. IEEE Transactions on Automatic ControlAC-25(5): 913–919.
24.
MarkovskyIDe MoorB (2005) Linear dynamic filtering with noisy input and output. Automatica41(1): 167–171.
25.
MirzaeeASalahshoorK (2012) Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller. Journal of Process Control22: 626–634.
26.
PattonRJChenJ (1993) A survey of robustness in quantitative model-based fault diagnosis. Applied Mathematics and Computer Science3(3): 399–416.
27.
PattonRJChenJ (1994) A review of parity space approaches to fault diagnosis for aerospace systems. Journal of Guidance Control & Dynamics17(2): 278–285.
28.
PattonRJChenJ (1996) Robust fault detection and isolation (FDI) systems. Control & Dynamical Systems74: 171–224.
29.
PattonRJChenJNielsenSB (1995) Model-based methods for fault diagnosis: some guidelines. Transactions of the Institute of Measurement and Control17(2): 73–83.
30.
ShamsherAN (2009) Linear and nonlinear polynomial based estimators. PhD thesis, Industrial Control Center, Strathclyde University.
31.
ThumatiBJagannathanS (2010) A model based fault detection and prediction scheme for nonlinear multivariable discrete-time systems with asymptotic stability guarantees. IEEE Transactions on Neural Networks21(3): 404–423.
32.
VenkatasubramanianVRRengaswamyKYKavuriSN (2003a) A review of process fault detection and diagnosis, part I: quantitative model-based methods. Computers & Chemical Engineering27: 293–311.
33.
VenkatasubramanianVRRengaswamyKYKavuriSN (2003b) A review of process fault detection and diagnosis, part II: qualitative models and search strategies. Computers & Chemical Engineering2: 313–326.
34.
WillskyAS (1976) A survey of design methods for failure detection in dynamic systems. Automatica12: 601–611.
35.
ZhangXPolycarpouMMParisiniT (2000) Abrupt and incipient fault isolation of nonlinear uncertain systems. Proceedings of the American Control Conference, Chicago, IL, pp. 3713–3717.
36.
ZhangXParisiniTPolycarpouM. M (2001) Integrated design of fault diagnosis and accommodation schemes for a class of nonlinear systems. Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, FL, pp. 1448–1453.
