The deep-sea submersible requires a reliable fault diagnosis system for safe operation. However, many current fault diagnosis methods focus on single subsystems, lacking the capability to diagnose concurrent failures across multiple systems. We propose a multi-subsystem fault diagnosis method with two key modules: a feature extraction module using 2D convolutions with 1D kernels to preserve sensor-specific temporal patterns, and an attention fusion module incorporating a squeeze-and-excitation mechanism to dynamically weight sensor importance. This architecture enables independent processing of multi-sensor data while maintaining computational efficiency and achieves multi-label fault classification across the electrical, hydraulic, propulsion, and control systems. In experiments conducted on a real-world operational dataset from a deep-sea submersible, our proposed method achieved the highest Macro F1-Score of 0.920 and demonstrated the fastest convergence among all compared methods.
FangXXiaoJGaoX, et al. Trajectory tracking of jiaolong submersible with velocity constraints via dual closed-loop control. Nonlinear Dyn2024; 112(24): 22161–22174.
2.
ZhuCWangWZhouB, et al. Weight-based distributed flocking control for multiple electric marine surface vehicles under fully intermittent communications. IEEE Internet Things J2025; 12(9): 11754–11766.
3.
HuangBSongYQinH, et al. Safety-enhanced formation maneuver control for electric vehicle with edge-weighted topology and reinforcement learning strategy. IEEE Trans Aerosp Electron Syst2025; 61: 14716–14731.
4.
FuxingWLongYLiT, et al. Asynchronous thruster fault detection for unmanned marine vehicles under multiple attacks. Nonlinear Dyn2025; 113(10): 11389–11402.
5.
LiuDCuiLWangH.Rotating machinery fault diagnosis under time-varying speeds: a review. IEEE Sens J2023; 23(24): 29969–29990.
6.
YangBLimDTanA.Vibex: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table. Expert Syst Appl2005; 28(4): 735–742.
7.
TranVTYangBSOhMS, et al. Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Expert Syst Appl2009; 36: 1840–1849.
8.
LiFLiRTianL, et al. Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions. Mech Syst Signal Process2019; 116: 462–479.
9.
YinZHouJ.Recent advances on svm based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing2016; 174: 643–650.
10.
ChuZLiZGuZ, et al. A fault diagnosis method for underwater thruster based on rfr-svm. Proc Inst Mech Eng Part M: J Eng Marit Environ2023; 237(2): 520–530.
11.
HuangKLiWFangH, et al. Iporf: A combined improved parrot optimizer algorithm and random forest for fault diagnosis in AUV. Ocean Eng2024; 313: 119665.
12.
Asadi MajdASametHGhanbariT. K-NN based fault detection and classification methods for power transmission systems. Prot Control Mod Power Syst2017; 2(1): 32.
13.
Bagci DasDBirantD. Gasel: genetic algorithm-supported ensemble learning for fault detection in autonomous underwater vehicles. Ocean Eng2023; 272: 113844.
14.
YaoFChenZLiuX, et al. Weak thruster fault detection for auv based on Bayesian network and hidden Markov model. Proc Inst Mech Eng Part M: J Eng Marit Environ2023; 237(2): 478–486.
15.
ChenYWangYYuY, et al. A fault diagnosis method for the autonomous underwater vehicle via meta-self-attention multi-scale cnn. J Mar Sci Eng2023; 11(6): 1121.
16.
QinXZhangWGaoS, et al. Sensor fault diagnosis of autonomous underwater vehicle based on LSTM. In: 2018 37th Chinese control conference (CCC), 2018, pp.6067–6072.
17.
JiDYaoXLiS, et al. Model-free fault diagnosis for autonomous underwater vehicles using sequence convolutional neural network. Ocean Eng2021; 232: 108874.
18.
HeDLaoZJinZ, et al. Train bearing fault diagnosis based on multi-sensor data fusion and dual-scale residual network. Nonlinear Dyn2023; 111(16): 14901–14924.
19.
HeDWuJJinZ, et al. AGFCN: a bearing fault diagnosis method for high-speed train bogie under complex working conditions. Reliab Eng Syst Saf2025; 258: 110907.
20.
ZhuDChengXYangL, et al. Information fusion fault diagnosis method for deep-sea human occupied vehicle thruster based on deep belief network. IEEE Trans Cybern2022; 52(9): 9414–9427.
21.
TsaiCMWangCSChungYJ, et al. Multisensor fusion time–frequency analysis of thruster blade fault diagnosis based on deep learning. IEEE Sens J2022; 22(20): 19761–19771.
22.
XiaSZhouXShiH, et al. A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV. Ocean Eng2022; 266: 112595.
23.
WangHXueGYuL, et al. Fault diagnosis of AUV propeller based on interference suppression clstm-cnn-attention. In: 2024 prognostics and system health management conference (PHM), 2024, pp.334–340.
24.
HuangBZhouXZhangC, et al. Event-triggered L2 performance control for affine formation of networked unmanned surface vehicles with intermittent communication. IEEE Trans Intell Transp Syst2025; 26(1): 962–973.
25.
ZhangYDingZLiuC, et al. Safety-oriented fault monitoring for manned deep-sea submersibles. In 2019 CAA symposium on fault detection, supervision and safety for technical processes (SAFEPROCESS), 2019, pp.864–869.
26.
HuJShenLSunG.Squeeze-and-excitation networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, 2018, pp.7132–7141.
27.
HeDZhaoJJinZ, et al. Prediction of bearing remaining useful life based on a two-stage updated digital twin. Adv Eng Inform2025; 65: 103123.
