Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Fault Diagnosis and Emergency Decision-making of Underwater High-speed Unmanned Vehicle
Emergency System Based on MFNN
Sha Xie, Zhemin Huang, Xingbang Pan, Ben Li, Guohua Xu,
Underwater Technology Laboratory, School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology
Wuhan, Hubei Province, China

established the dynamic model of autonomous underwater vehicle byusing the method of artificial neural network, and used the model
matching part to detect the output value of the actual sensor. Fuzzy logic
algorithm is a powerful tool to deal with uncertainty and nonlinear
problems. For example, fuzzy controller is used to complete thediagnosis of power system of boiling water reactor nuclear power plantby Gmytrasiewicz et al. (1990) The MFNN method, which combines
fuzzy logic and neural network, is suitable for the risk analysis anddecision-making of underwater high-speed unmanned craft withuncertainty and complexity.

ABSTRACTThis paper proposes an intelligent fault diagnosis and critical decisionmaking method based on MFNN. The fault tree model of underwater
high-speed unmanned vehicle is established, and the fault diagnosis ofunderwater high-speed unmanned vehicle is carried out based on theadaptive learning and fuzzy reasoning ability of Mamdani fuzzy neuralnetwork (MFNN). Then the fault diagnosis and intelligent decisionmaking evaluation of underwater high-speed unmanned craft are carriedout to ensure the safety of underwater high-speed unmanned craft.
Finally, the proposed intelligent fault analysis and decision-making
method is verified by numerical simulation. The simulation results prove
the feasibility and effectiveness of the intelligent fault treatment system.

Due to the complex underwater operation environment, there are manykinds of situations where the self-propelled model needs emergency
recovery in case of danger. At present, most of them carry out fault
diagnosis for a single fault type, such as power system fault, which is far
from enough. In order to ensure the navigation safety of the selfpropelled model, it is necessary to comprehensively consider all faults ofthe self-propelled model and design a perfect fault diagnosis strategy.
The diagnosis scope covers all fault events of the self-propelled model.
According to the results of fault detection, it is necessary to makereasonable emergency decisions to deal with fault events with different
priorities with the best strategy.

KEY WORDS: Underwater high-speed unmanned boat; Fault treemodel; MFNN; Fault diagnosis; Emergency decision

INTRODUCTION
Underwater high-speed unmanned craft belongs to the category of AUV.
The research of AUV emergency system includes the functional designof emergency system and the design of emergency actuator. The
functional design mainly focuses on fault diagnosis and emergency
decision-making. When underwater high-speed unmanned craft isoperating, it is limited by the complex environment and handlingcharacteristics of operation. Some faults may occur within a certain
operation time, resulting in the failure of the AUV to continue to worknormally, which needs to be recycled automatically.

In this paper, first analyzes the control object, lists the possible faults ofeach system of underwater high-speed unmanned craft, divides thepossible faults into several categories according to the classification form,
establishes the fault tree model to complete the fault judgment, and thensends the fault information to the fault diagnosis subsystem based on
MFNN model, combined with the advantages of fuzzy logic and neuralnetwork, it is used to analyze the system risk of underwater high-speed
unmanned craft. Finally, the intelligent risk of the system will be
evaluated and the corresponding decision will be made.

In recent years, many scholars at home and abroad have applied
intelligent methods to the field of fault diagnosis and intelligent decisionmaking of underwater vehicles, and achieved some results, mainly
including grey prediction model, neural network method, fuzzy logic
algorithm, sliding mode algorithm and so on. Neural network method is
widely used in system prediction and fault diagnosis because of its
adaptive learning ability. For example, Alexander Inzartsev et al. (2016)used artificial intelligence method to diagnose the fault of AUV, anddecided the navigation of AUV according to the diagnosis results. In thesame year, Raanan et al. (2016) adopted an online topic model to detectand classify AUV faults. The results show that this method has no false
detection. Liu et al. (2012) proposed a fault diagnosis method based oncerebellar model articulation controller (CMAC) neural network toobtain online learning of underwater thrusters. Takai et al. (1999)

EMERGENCY SYSTEM OF UNDERWATER HIGH-SPEEDUNMANNED CRAFTThe emergency system is an independent control system withindependent controller. The power supply of the emergency system
adopts the form of redundant power supply and redundant signal drive,
which is to ensure that the emergency system still has the function of
performing emergency action in case of main power loss. The emergency
system is mainly composed of power supply, emergency computer,driving signal switch and emergency actuator. All the system

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