This paper presents a methodology for satellite sensors' fault detection and diagnosis (FDD) in a fault-tolerant attitude determination system using a nonlinear interacting-multiple-model approach (IMM). Moreover, the fault-tolerant attitude determination system utilizes the IMM approach in the presence of sensors' total fault to estimate faulty sensors and attitude states. This methodology relies on the most conventional satellite attitude sensors including magnetometer, sun sensor, and gyroscope measurements. The presented scheme leads to a significant computational time-saving method due to considering a pre-filter Kalman for fault detection, hence, the proposed FDD approach consists of two stages. In the first stage, the pre-filter Kalman detects any sensor fault and attitude states. In contrast, in the next stage, fault diagnosis and optimal attitude estimation are obtained through the IMM approach. Therefore, the IMM algorithm will be activated to extract the optimal estimation and identify faulty sensor(s). A Monte Carlo type approach is used to verify this technique for various initial conditions. Through numerical and Monte Carlo simulations, it is shown that the proposed scheme is robust against sensors' total fault, and the system is capable of detecting and isolating accurately the faulty sensor. Hardware in the loop tests using satellite dynamics simulator are also used to validate the presented system performance.
YunXEricBachmannR. Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking. IEEE Trans Robot2006; 22(6): 1216–1227.
2.
SzczesnaAPruszowskiP. Model-based extended quaternion Kalman filter to inertial orientation tracking of arbitrary kinematic chains. SpringerPlus2016; 5: 1965. DOI: 10.1186/s40064-016-3653-8.
3.
RenaudinVCombettesC. Magnetic, acceleration fields and gyroscope quaternion (MAGYQ)-Based attitude estimation with smartphone sensors for indoor pedestrian navigation. Sensors2014; 14(12): 22864–22890. DOI: 10.3390/s141222864.
4.
MansoorSBhattiUIBhattiAI, et al.Improved attitude determination by compensation of gyroscopic drift by use of accelerometers and magnetometers. Measurement2019; 131: 582–589. DOI: 10.1016/j.measurement.2018.08.067.
5.
HajiyevCÇildenDSomovY. Integrated SVD/EKF for small satellite attitude determination and rate gyro bias estimation. IFAC-PapersOnLine2015; 48(9): 233–238.
6.
NiSZhangC. Attitude determination of nano satellite based on gyroscope, sun sensor and magnetometer. Proced Eng2011; 15: 959–963.
7.
HajiyevCÇildenDSomovY. Integrated SVD/EKF for small satellite attitude determination and rate gyro bias estimation. IFAC-PapersOnLine2015; 48(9): 233–238.
8.
ByeonSMokS-HWooH, et al.Sensor-fault tolerant attitude determination using two-stage estimator. Adv Space Res2019; 63(11): 3632–3645. DOI: 10.1016/j.asr.2019.02.007.
9.
BaeJKimY. Attitude estimation for satellite fault tolerant system using federated unscented Kalman filter. Int’l J Aeronaut Space Sci2010; 11(2): 80–86. DOI: 10.5139/IJASS.2010.11.2.080.
10.
WangJHeZPanX, et al.Fault diagnosis for satellite’s attitude determination system based on model error prediction and EMD. Washington, DC, USA: American Control Conference (ACC), 2013.
11.
SokenHEHajiyevCSakaiS. Robust Kalman ltering for small satellite attitude estimation in the presence of measurement faults. Eur J Control2014; 20(2): 64–72. DOI: 10.1016/j.ejcon.2013.12.002.
12.
NasrolahSSAbdollahiF. Sensor fault detection and recovery in satellite attitude control. Acta Astronautica2018; 145: 275–283. DOI: 10.1016/j.actaastro.2018.01.002.
13.
BolandiHAbediMNasrollahiS. Design of an analytical fault tolerant attitude determination system using Euler angles and rotation matrices for a three-axis satellite. Proc Inst Mech Eng G: J Aerosp Eng2014; 228(5): 706–726.
14.
PirmoradiFNSassaniFde SilvaCW. Fault detection and diagnosis in a spacecraft attitude determination system. Acta Astronautica2009; 65(5–6): 710–729. DOI: 10.1016/j.actaastro.2009.03.002.
15.
MehraRKRagoCSeereeramS. Failure detection and identification using a nonlinear interactive multiple model (IMM) filtering approach with aerospace applications. IFAC Proc1997; 30(11): 407–412.
16.
TalebiHAKhorasanKTafazoliS. 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 Trans Neural Netw2009; 20(1): 1045–9227.
17.
AdnaneAFoitihZAMohammedMA, et al.Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data. Adv Space Res2018; 61(4): 1143–1157. DOI: 10.1016/j.asr.2017.12.007.
18.
LimJKChoiKRParkCG. Fault tolerant attitude estimation for an LEO satellite using a multi-hypothesis filter. Int J Control Autom Syst2012; 10: 1070–1076. DOI: 10.1007/s12555-012-0526-y.
19.
RahimiAKumarKDAlighanbariH. Fault isolation of reaction wheels for satellite attitude control. IEEE Trans Aerosp Electron Syst2020; 56(1): 610–629. DOI: 10.1109/TAES.2019.2946665.
20.
ZhangCYangHJiangB. fault tolerant model reduction and actuator/sensor placement for flexible spacecraft. IEEE Trans Aerosp Electron Syst; 57(2): 1334–1344. DOI: 10.1109/TAES.2020.3031756.
21.
YangWWangJLiS, et al.Attitude fault tolerant control for satellite under actuator fault and inertial sensor fault. In: 2020 39th Chinese control conference (CCC), Shenyang, China, 27–29 July 2020, pp. 3439–3443. DOI: 10.23919/CCC50068.2020.9188574.
22.
LongDWenXZhangW, et al.Recurrent neural network based robust actuator and sensor fault estimation for satellite attitude control system. IEEE Access2020; 8: 183165–183174. DOI: 10.1109/ACCESS.2020.3029066.
23.
SimaniSFantuzziC. Fault diagnosis in power plant using neural networks. Inf Sci2000; 127: 125–136. DOI: 10.23919/ECC.1999.7099658.
GoelPDedeogluGRoumeliotisSI, et al.Fault detection and identification in a mobile robot using multiple model estimation and neural network. In: Proceedings of the 2000 IEEE international conference on robotics & automation, San Francisco, CA, USA, 24–28 April 2000. DOI: 10.1109/ROBOT.2000.846370.
26.
RkhissiYGhommamJBoukhniferM, et al.Two current sensor fault detection and isolation schemes for induction motor drives using algebraic estimation approach. Math Comput Simulation2019; 157: 39–62. DOI: 10.1016/j.matcom.2018.09.010.
27.
Namigtle-JiménezAEscobar-JiménezRFGómez-AguilarJF, et al.Online ANN-based fault diagnosis implementation using an FPGA: application in the EFI system of a vehicle. ISA Trans2019; 100: 358–372. DOI: 10.1016/j.isatra.2019.11.003.
28.
NaviMMeskinNDavoodiM. Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA. J Process Control2018; 64: 37–48. DOI: 10.1016/j.jprocont.2018.02.002.
29.
NiuGXiongLQinX, et al.Fault detection isolation and diagnosis of multi-axle speed sensors for high-speed trains. Mech Syst Signal Process2019; 131: 183–198. DOI: 10.1016/j.ymssp.2019.05.053.
30.
QiRZhangJ. Data-driven fault diagnosis and prognosis for process faults using principal component analysis and extreme learning machine. In: 2020 IEEE 18th international conference on industrial informatics (INDIN), Warwick, United Kingdom, 20–23 July 2020, pp. 775–780. DOI: 10.1109/INDIN45582.2020.9442177.
31.
GarciaCBAValeSRCMartins-FilhoLS, et al.Validation tests of attitude determination software for nanosatellite embedded systems. Measurement2018; 116: 391–401. DOI: 10.1016/j.measurement.2017.11.040.
32.
DasMSadhuSGhoshalTK. Fault detection and isolation of satellite inertial instrumentation using nonlinear adaptive filters. J Aerosp Inf Syst2015; 12(2): 286–292. DOI: 10.2514/1.I010180.
33.
FonodRHenryDCharbonnelC, et al.Position and attitude model-based thruster fault diagnosis: a comparison study. J Guidance, Control Dyn2015; 38(6): 1012–1026. DOI: 10.2514/1.G000309.
34.
QianMYiHZhengZ. Integrated fault tolerant attitude control approach for satellite attitude system with sensor faults. Optim Control Appl Meth2019; 41: 555–570. DOI: 10.1002/oca.2557.
35.
ZhouJLiXLiuR, et al.A scheme of satellite multi-sensor fault-tolerant attitude estimation. Trans Inst Meas Control2016; 38: 1053–1063.
36.
WangX. Active fault tolerant control for unmanned underwater vehicle with sensor faults. IEEE Trans Instrumentation Meas2020; 69(12): 9485–9495. DOI: 10.1109/TIM.2020.3003108.
37.
KordestaniMSafaviAASharafiN, et al.Fault tolerant control of Rhine-Meuse delta water system: a performance assessment based approach. In: 2019 International conference on power generation systems and renewable energy technologies (PGSRET), Istanbul, Turkey, 26–27 August 2019, pp. 1–8. DOI: 10.1109/PGSRET.2019.8882720.
38.
WangHKangYYaoL, et al.fault diagnosis and fault tolerant control for T–S fuzzy stochastic distribution systems subject to sensor and actuator faults. IEEE Trans Fuzzy Syst2021; 29(11): 3561–3569. DOI: 10.1109/TFUZZ.2020.3024659.
39.
KordestaniMSalahshoorKSafaviAA, et al.An adaptive passive fault tolerant control system for a steam turbine using a PCA based inverse neural network control strategy. In: 2018 world automation congress (WAC), Stevenson, WA, USA, 03–06 June 2018, pp. 1–6. DOI: 10.23919/WAC.2018.8430414.
40.
CastellaniFAstolfiDNatiliF. SCADA data analysis methods for diagnosis of electrical faults to wind turbine generators. Appl Sci2021; 11: 3307. DOI: 10.3390/app11083307.
41.
RezamandMKordestaniMCarriveauR, et al.A new hybrid fault detection method for wind turbine blades using recursive PCA and wavelet-based PDF. IEEE Sensors J2020; 20(4): 2023–2033. DOI: 10.1109/JSEN.2019.2948997.
LeffertsEJMarkleyFLShusterMD. Kalman filter for spacecraft attitude estimation. J Guidance, Control Dyn1982; 5(5): 417–429.
44.
ZhangYLiXR. Detection and diagnosis of sensor and actuator failures using IMM estimator. IEEE Trans Aerosp Electron Syst1998; 34: 1293–1313. DOI: 10.1109/7.722715.
45.
SachsL.Applied Statistics: A handbook of Techniques. New York: Springer, 1982.
