This paper introduces a statistical fault detection and identification (FDI) scheme for aircraft systems, which uses flight attitude data rather than information from purposely developed physical or virtual sensors. The scheme is based on the modelling of relationships among the considered data via stochastic Time-dependent Functionally Pooled Non-linear AutoRegressive with Exogenous excitation (TFP-NARX) representations. These are globally valid inside a flight regime and under various considered environmental conditions, thanks to the pooling technique used for their identification. Moreover, due to the TFP-NARX coefficients being a function of time-dependent quantities, high modelling accuracy is achieved. The scheme's operation involves identifying nominal TFP-NARX models of relationships among the attitude data from an aircraft operating in a healthy state. Owing to fault occurrence, these relationships may change. Then, an in-flight comparison of the nominal and the current aircraft dynamics provides fault-related information, which is statistically evaluated for FDI purposes. The scheme's performance and robustness are assessed with numerous flights conducted throughout a flight regime under various manoeuvring settings and turbulence levels.
HengyD.FrankP.Component failure detection via nonlinear state observers. In Proceedings of the IFAC Workshop on Fault Detection and Safety in Chemical Plants, Kyoto, Japan, September 1986, pp. 153–157.
6.
GertlerJ.Fault detection and diagnosis in engineering systems, 1998 (Marcel Dekker, New York).
7.
RagoC.PrasanthR.MehraR.FortenbaughR.Failure detection and identification and fault tolerant control using the IMM-KF with applications to the Eagle-Eye UAV. In Proceedings of the 35th IEEE Conference on Decision and Control, Tampa, FL, December 1998, pp. 4208–4213.
8.
ZhangY.LiX.Detection and diagnosis of sensor and actuator failures using IMM estimator. IEEE Trans. Aerosp. Electron. Syst., 1998, 34(4), 1293–1313.
9.
ZhangY.JiangJ.Integrated active fault-tolerant control using IMM approach. IEEE Trans. Aerosp. Electron. Syst., 2001, 37(4), 1221–1235.
10.
BoskovicJ.MehraR.Multiple-model adaptive flight control scheme for accommodation of actuator failures. J. Guid. Control Dyn., 2002, 25(4), 712–724.
11.
AttarM.WahnonE.ChaimovitzD.Advanced flight control technologies for UAVs. In Proceedings of the 2nd AIAA Unmanned Unlimited Conference, Workshop and Exhibit, San Diego, California, September 2003, AIAA paper-2003-6537.
12.
NapolitanoM.NeppachC.CasdorphV.NaylorS.InnocentiM.SilvestriG.Neural network based scheme for sensor failure detection, identification and accommodation. J. Guid. Control Dyn., 1995, 18(6), 1280–1286.
13.
NapolitanoM.AnY.SeanorB.A fault tolerant flight control system for sensor and actuator failures using neural networks. Aircraft Design, 2000, 3(1), 103–128.
14.
De PersisC.De SanctisR.IsidoriA.Nonlinear actuator fault detection and isolation for a VTOL aircraft. In Proceedings of the American Control Conference, Arlington, VA, USA, June 2001, pp. 4449–4454.
15.
De PersisC.IsidoriA.A geometric approach to non-linear fault detection and isolation. IEEE Trans. Autom. Control, 2001, 46(6), 853–865.
16.
AmatoF.CosentinoC.MatteiM.PaviglianitiG.A direct/functional redundancy scheme for fault detection and isolation on an aircraft. Aerosp. Sci. Technol., 2006, 10(4), 338–345.
17.
HammouriH.KinnaertM.El YaagoubiE.Observer-based approach to fault detection and isolation for nonlinear systems. IEEE Trans. Autom. Control, 1999, 44(10), 1879–1884.
18.
BonfèM.CastaldiP.GeriW.SimaniS.Fault detection and isolation for on-board sensors of a general aviation aircraft. Int. J. Adapt. Control Signal Process., 2006, 20(8), 381–408.
19.
ZhangX.PolycarpouM.ParisiniT.Sensor bias fault isolation in a class of nonlinear systems. IEEE Trans. Autom. Control, 2005, 50(3), 370–376.
20.
ZhangX.PolycarpouM.ParisiniT.A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems. IEEE Trans. Autom. Control, 2002, 47(4), 576–593.
21.
SamaraP.SakellariouJ.FouskitakisG.FassoisS.Detection of sensor abrupt faults in aircraft control systems. IEEE Trans. Control Syst. Technol., 2007 (in press).
22.
DimogianopoulosD.HiosJ.FassoisS.Fault detection and isolation in aircraft systems using stochastic nonlinear modelling of flight data dependencies. In Proceedings of the Mediterranean Conference on Control and Automation MED 2006, Ancona, Italy, June 2006, paper TLA2.
23.
DimogianopoulosD.HiosJ.FassoisS.Statistical fault detection and identification in aircraft systems via functionally pooled nonlinear modelling of flight data dependencies. In Proceedings of the 25th International Congress of the Aeronautical Sciences ICAS 2006, Hamburg, Germany, August 2006, ICAS paper 2006-6.2.4.
24.
DimogianopoulosD.HiosJ.FassoisS.FDI for aircraft systems using stochastic pooled NARMAX representations: Design and assessment. IEEE Trans. Control Syst. Technol., 2007 (submitted for publication).
25.
AxelssonT.Diagnosis system conceptual design utilizing structural methods applied on a UAV's fuel system. MS Thesis, Department of Electrical Engineering, Linköpings Universitet, Sweden, 2004.
26.
ZhangQ.BassevilleM.BenvenisteA.Fault detection and isolation in nonlinear dynamic systems: A combined input-output and local approach. Automatica, 1998, 34(11), 1359–1374.
27.
SamaraP.FouskitakisG.SakellariouJ.FassoisS.Aircraft angle-of-attack virtual sensor design via a functional pooling NARX methodology. In Proceedings of the European Control Conference, University of Cambridge, UK, September 2003, paper 309.
KopsaftopoulosF.FassoisS.Identification of stochastic systems under multiple operating conditions: the vector dependent FP-ARX parametrization. In Proceedings of the Mediterranean Conference on Control and Automation MED 2006, Ancona, Italy, June 2006, paper WM4-4.
30.
BoxG.JenkinsG.ReinselG.Time series analysis forecasting and control, 3rd edition, 1994 (Prentice Hall, Englewood Cliffs, New Jersey).
31.
BillingsS.ChenS.KorenbergM.Identification of MIMO nonlinear systems using a forward-regression orthogonal estimator. Int. J. Control, 1989, 49(6), 2157–2189.
32.
HaberR.UnbehauenH.Structure identification of nonlinear dynamic systems - a survey on I/O approaches. Automatica, 1990, 26(4), 651–677.
33.
SöderströmT.StoicaP.System identification, 1989 (Prentice Hall Int., London).
34.
PapoulisA.Probability, random variables and stochastic processes, 1991 (McGraw-Hill Inc., New York).
