Review on uncertainty analysis and information fusion diagnosis of aircraft control system

doi:10.23919/JSEE.2024.000070

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (5): 1245-1263.doi: 10.23919/JSEE.2024.000070

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Review on uncertainty analysis and information fusion diagnosis of aircraft control system

Keyi ZHOU¹(), Ningyun LU¹^,*(), Bin JIANG¹(), Xianfeng MENG²()

¹ College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
² Avic Xi’an Flight Automatic Control Research Institute, Xi’an 710076, China

Received:2023-03-15 Online:2024-10-18 Published:2024-11-06
Contact: Ningyun LU E-mail:koeyzhouky@nuaa.edu.cn;luningyun@nuaa.edu.cn;binjiang@nuaa.edu.cn;mengyangxiuzi@163.com
About author:
ZHOU Keyi was born in 1993. She received her M.S. degree in control science and engineering from Xi’an University of Technology, Xi’an, China, in 2018. She is currently pursuing her Ph.D. degree in control theory and control engineering with the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. Her research interests include information fusion and fault diagnosis. E-mail: koeyzhouky@nuaa.edu.cn

LU Ningyun was born in 1978. She received her Ph.D. degree from Northeastern University, Shenyang, China, in 2004. She is currently a professor with the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China. Her research interests include data-driven fault prognosis and diagnosis and their applications to various industrial processes. E-mail: luningyun@nuaa.edu.cn

JIANG Bin was born in 1966. He received his Ph.D. degree in automatic control from Northeastern University, Shenyang, China, in 1995. He is currently a professer in Nanjing University of Aeronautics and Astronautics, Nanjing, China. His research interests include fault diagnosis and fault tolerant control and their applications in aircraft, satellites, and high-speed trains. E-mail: binjiang@nuaa.edu.cn

MENG Xianfeng was born in 1990. He received his B.S. degree in automation and M.S. degree in guidance, navigation and control from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2013 and 2016, respectively. His research interests include modeling of control systems, fault diagnosis, and preliminary design of the helicopter. E-mail: mengyangxiuzi@163.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62273176), the Aeronautical Science Foundation of China (20200007018001) and the China Scholarship Council (202306830096).

Abstract

Abstract:

In the aircraft control system, sensor networks are used to sample the attitude and environmental data. As a result of the external and internal factors (e.g., environmental and task complexity, inaccurate sensing and complex structure), the aircraft control system contains several uncertainties, such as imprecision, incompleteness, redundancy and randomness. The information fusion technology is usually used to solve the uncertainty issue, thus improving the sampled data reliability, which can further effectively increase the performance of the fault diagnosis decision-making in the aircraft control system. In this work, we first analyze the uncertainties in the aircraft control system, and also compare different uncertainty quantitative methods. Since the information fusion can eliminate the effects of the uncertainties, it is widely used in the fault diagnosis. Thus, this paper summarizes the recent work in this aera. Furthermore, we analyze the application of information fusion methods in the fault diagnosis of the aircraft control system. Finally, this work identifies existing problems in the use of information fusion for diagnosis and outlines future trends.

Key words: aircraft control system, sensor networks, information fusion, fault diagnosis, uncertainty

Keyi ZHOU, Ningyun LU, Bin JIANG, Xianfeng MENG. Review on uncertainty analysis and information fusion diagnosis of aircraft control system[J]. Journal of Systems Engineering and Electronics, 2024, 35(5): 1245-1263.

Figures/Tables 3

Fig 1

Fig 2

Table 1

Comparison of uncertainty quantification methods"

Method	Description	Characteristic
Fuzzy set theory	Uncertainty can be described by membership function and fuzzy set $ K = \{ (u,K(u))\|u \in U\} $. K is the fuzzy set on universe U, $ K(u) $ is the membership function of fuzzy set K, and u is the membership degree of K.	The classical binary logic is extended to multivalued logic. It is difficult to accurately construct the membership function, which needs to rely on statistics or expert experience.
Possibility theory	It mainly aims at events with ambiguous denotation. When the statistical characteristics of events cannot be obtained, they are described by probability distribution.	This theory is another framework based on fuzzy sets, which describes the trust degree of elements in power set. The theory is not widely used.
Rough set theory	The imprecise knowledge is approximated by the known knowledge, and the knowledge is expressed as a quaternion ordered group: $ K = (U,C \cup D,{V_{\text{a}}},{I_{\text{a}}}) $. U represents universe. $ C \cup D $ is the whole of attributes, which is a finite and nonempty set. $ {V_a}:a \in C \cup D $ indicates the value range of attribute $ a $. $ {I_a}:U \to {V_a} $, $ {I_a}(x) $ indicates the value of object x in attribute a.	The existing data set can be directly reduced to obtain decision rules, so the description or processing of uncertain and imprecise problems is more objective. This theory only analyzes the information contained in the dataset, and the scope of information processing is limited.
Random set theory	The random set is an extension of the random variable.	This theory provides a theoretical basis for the unified representation of multi-source heterogeneous uncertain information. It is often used to study heterogeneous uncertain information.
Granular computing	Different granulation methods are used to build information granules, and uncertain complex problems are abstractly transformed into subproblems. Uncertainty in the structure is represented by different models.	The same problem can be solved from different granularity, which can efficiently solve complex problems. The construction of reasonable information granules is diversified and depends on experience.
Bayesian theory	A₁,A₂,···,A_m is a partition of sample space S. The observation results of n information sources are respectively B₁, B₂,···,B_n. The total posterior probability of n information sources for decision-making can be obtained: $ \Pr \left( {{A_i}\mid {B_1} \wedge {B_2} \wedge \cdots \wedge {B_n}} \right) = \frac{{\displaystyle\mathop \prod \limits_{k = 1}^n \Pr \left( {{B_k}\mid {A_i}} \right)\Pr \left( {{A_i}} \right)}}{{\displaystyle\sum\limits_{j = 1}^m {\mathop \Pi \limits_{k = 1}^n } \Pr \left( {{B_k}\mid {A_j}} \right)\Pr \left( {{A_j}} \right)}},{\text{ }}i = 1,2 \cdots ,m $	The theory requires that the observation information is independent of each other. Prior probability and conditional probability are usually difficult to obtain.
Evidence theory	The evidence interval is composed of trust function Bel(A) and likelihood function Pl(A). Evidence interval is used to describe uncertainty. The theory deals with uncertainty through evidence and combination.	The theory can be applied to various qualitative and quantitative propositions and has a wide range of applications.

Table 1

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