The article studies the fault diagnosis of the first-stage power system of a carrier rocket made up of multiple engines. It establishes the simplified 6-DOF nonlinear model and uses the extended Kalman filter to generate residual errors. The quadratic receding time-domain algorithm is used to detect faults, and estimation vector fault characteristics are used to estimate precisely the thrust loss degree. Faults are located with the direction of abrupt change in overload fault. The simulation results show that the method can quickly detect engine faults, estimate the thrust loss degree with fault estimation values, and locate precisely the serial number of the faulty engine of the power system made up of multiple engines. The method boosts the development of the online health management technology of the power system of a carrier rocket.
TanYHZhaoJChenJH, et al.Progress in technology of main liquid rocket engines of launch vehicles in China. Aerosp China2020; 21(2): 23–30. English Version.
2.
GaoSZhaoZWZhangW, et al.Fault diagnosis for satellite attitude control system with using extended Kalman filter. In: Proceedings of the 38th Chinese control conference, Guangzhou, China, 17 October 2019, 2019.
3.
ZhengJPanHYangS, et al.Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis. Signal Process2017; 130: 305–314.
4.
PowarRU. Fault diagnosis of roller bearing using vibration signals through ARMA features and tree family classifier. Int J Eng Res Technol2015; 4(7): 688–692.
5.
MeyerCMZakrajsekJF. Rocket engine failure detection using system identification techniques. In: 26th joint propulsion conference, Orlando, FL, USA, 16–18 July 1990. National Aeronautics and Space Administration. AIAA-90-1993.
6.
MaSCaiWLiuW, et al.A lighted deep convolutional neural network based fault diagnosis of rotating machinery. Sensors2019; 19(10): 2381–2401.
7.
WilliamsRBJrParlosAG. Adaptive state filtering for space shuttle main engine turbine health monitoring. J Spacecraft Rockets2003; 40(1): 101–109.
8.
DuyarAMerrillW. A failure diagnosis system based on a neural network classifier for the space shuttle main engine. In: The 29th conference on decision and control, Honolulu, Hi, USA, 5–7 December 1990, 1990.
9.
MoshouDBravoCWestJ, et al.Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Comput Electron Agric2004; 44: 173–188.
10.
HanYongHZengQiS. Fault detection and isolation of flight based on robust observer. In: The 5th world congress on intelligent control and automation, Hangzhou, China, 15–19 June 2004, 2004.
11.
WangDLumK-Y. Adaptive unknown input observer approach for aircraft actuator fault detection and isolation. Int J Adaptive Control Signal Process2007; 21: 31–48.
12.
DuDYangYZhaoH, et al.Robust fault diagnosis observer design for uncertain switched systems. Int J Control Autom Syst2020; 18: 3159–3166.
13.
ChenRHSpeyerJL. A generalized least-squares fault detection filter. Int J Adapt Control Signal Process2000; 14: 747–757.
14.
NabiA. Application of least squares parameter estimation techniques to fault detection. Int J Eng Trends Technol2013; 4: 447–450.
15.
AthamenaBHouhamdiZMuhairatM. Fault detection and isolation in dynamic systems using statistical local approach and hybrid least squares algorithm. Am J Appl Sci2007; 4(12): 977–986.
16.
DarenYJianboW. Leak fault detection of liquid rocket engine based on strong tracking filter. J Propul Power2002; 18(2): 280–283.
17.
WangLWuLGuanY, et al.Online sensor fault detection based on an improved strong tracking filter. Sensors2015; 15: 4578–4591.
18.
MaJNiS-HXieW-J, et al.An improved strong tracking multiple-model adaptive estimation: a fast diagnosis algorithm for aircraft actuator fault. Trans Inst Meas Control2016; 38(7): 846–854.
19.
BiswasSKLiQDempsterAG. Computationally efficient unscented Kalman filtering techniques for launch vehicle navigation using a space-borne GPS receiver. In: Proceedings of the 29th international technical meeting of the satellite division of The institute of navigation (ION GNSS+ 2016), Portland, OR, USA, 12–16 September 2016, 2016.
20.
ChenKJLiuLHMengYH. Launch Vehicle Flight Dynamics and Guidance. Beijing, China: National Defense Industry Press, 2014. (in Chinese).
