Useful life prediction using a stochastic hybrid automata model for an ACS multi-gyro subsystem

doi:10.21629/JSEE.2019.01.15

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (1): 154-166.doi: 10.21629/JSEE.2019.01.15

• Control Theory and Application • Previous Articles Next Articles

Useful life prediction using a stochastic hybrid automata model for an ACS multi-gyro subsystem

Yuehua CHENG^1,*(), Liang JIANG¹(), Bin JIANG²(), Ningyun LU²()

¹ College of Astronautics Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
² College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received:2017-10-12 Online:2019-02-27 Published:2019-02-27
Contact: Yuehua CHENG E-mail:chengyuehua@nuaa.edu.cn;JL82W2@163.com;binjiang@nuaa.edu.cn;luningyun@nuaa.edu.cn
About author:CHENG Yuehua was born in 1977. She is an associate professor in Nanjing University of Aeronautics and Astronautics. Her research interests include tolerant control and life prediction for satellite attitude control systems. E-mail:chengyuehua@nuaa.edu.cn|JIANG Liang was born in 1992. He is a Ph.D. candidate of Nanjing University of Aeronautics and Astronautics. His re-search interest is life prediction for satellite attitude control systems. E-mail:JL82W2@163.com|JIANG Bin was born in 1966. He is a professor and the dean of the College of Automation Engineering in Nanjing University of Aeronautics and Astronautics. He now serves as an associate editor for IEEE Trans. on Control Systems Technology; International Journal of System Science; International Journal of Control, Automation and Systems; Acta Automatica Sinica, Systems Engineering and Electronics, etc. His research interests include fault diagnosis and fault tolerant control and their applications. E-mail:binjiang@nuaa.edu.cn|LU Ningyun was born in 1978. She is a professor in Nanjing University of Aeronautics and Astronautics. Her research interests include fault diagnosis and health management of complex engineering system. E-mail:luningyun@nuaa.edu.cn
Supported by:
the Fundamental Research Funds for the Central Universitie(2016083);This work was supported by the Fundamental Research Funds for the Central Universities (2016083)

Abstract

Abstract:

A useful life prediction method based on the integration of the stochastic hybrid automata (SHA) model and the frame of the dynamic fault tree (DFT) is proposed. The SHA model can incorporate the orbit environment, work modes, system configuration, dynamic probabilities and degeneration of components, as well as spacecraft dynamics and kinematics. By introducing the frame of DFT, the system is classified into several layers, and the problem of state combination explosion is artfully overcome. An improved dynamic reliability model (DRM) based on the Nelson hypothesis is investigated to improve the defect of cumulative failure probability (CFP), which is used to address the failure probability of components in the SHA model. The simulation using the Monte-Carlo method is finally conducted on two satellites, which are deployed with the same multi-gyro subsystem but run on different orbits. The results show that the predicted useful life of the attitude control system (ACS) with consideration of abrupt failure, degradation, and running environment is quite different between the two satellites.

Key words: useful life prediction, stochastic hybrid automata (SHA), multi-gyro subsystem, dynamic fault tree (DFT), dynamic reliability

Yuehua CHENG, Liang JIANG, Bin JIANG, Ningyun LU. Useful life prediction using a stochastic hybrid automata model for an ACS multi-gyro subsystem[J]. Journal of Systems Engineering and Electronics, 2019, 30(1): 154-166.

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References 25

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Method	Number of SHA	Number of DFT	Number of discrete states	Number of discrete states (manual modeling)
SHA	1	0	2 712	2 712
DFT-SHA	10	1	2 712	9

$\mathcal{A}_G $	$s_{G, i} $	$e_{G, ij} $	$P_{G, ij} $	$G_{G, ij} (\cdot)$	$s_{G, j} $
$A_{G, 0} $	/	/	/	/	$s_{G, 1} $
$A_{G, 12} $	$s_{G, 1} $	$e_{G, 12} $	/	$G_{G, 12} (t) = 1-w_{G, 1} $	$s_{G, 2} $
$A_{G, 21} $	$s_{G, 2} $	$e_{G, 21} $	/	$G_{G, 12} (t) = w_{G, 1} $	$s_{G, 1} $
$A_{G, 13} $	$s_{G, 1} $	$e_{G, 13} $	$x_{G, 2} $	/	$s_{G, 3} $
$A_{G, 23} $	$s_{G, 2} $	$e_{G, 23} $	$x_{G, 2} $	/	$s_{G, 3} $

$\mathcal{A}_H $	$s_{H, i} $	$e_{H, ij} $	$G_{H, ij} (\cdot)$	$s_{H, j} $
$A_{H, 0} $	/	/	/	$s_{H, 1} $
$A_{H, 12} $	$s_{H, 1} $	$e_{H, 12} $	$G_{H, 12} (t) = w_{H, 4} (t)-3$	$s_{H, 2} $
$A_{H, 13} $	$s_{H, 1} $	$e_{H, 13} $	$G_{H, 13} (t) = w_{H, 2} (t)-3$	$s_{H, 3} $
$A_{H, 24} $	$s_{H, 2} $	$e_{H, 24} $	$G_{H, 24} (t) = w_{H, 4} (t)-3$	$s_{H, 4} $
$A_{H, 34} $	$s_{H, 3} $	$e_{H, 34} $	$G_{H, 34} (t) = w_{H, 4} (t)-3$	$s_{H, 4} $

Sat.	Number of samples	Number of abrupt failure	Average UL/d	Standard deviation of UL/ d	Kurtosis/Skewness	Span/ d
Sat. A	500	1	2 366.76	298.37	0.18/0.44	1 608–3 462
Sat. B	500	8	2 013.19	208.86	0.97/0.49	1 413–2 972

Sat.	< 1 800 d	< 2 000 d	< 2 200 d	< 2 400 d	< 2 600 d
Sat.A	1.20%	10.00%	32.40%	55.80%	80.00%
Sat.B	13.8%	51.00%	82.80%	96.00%	99.80%

Useful life prediction using a stochastic hybrid automata model for an ACS multi-gyro subsystem

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