RESEARCH PAPER
A Physical Feature Residual Model for Actuator Fault Detection of Autonomous Underwater Vehicles
1
Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts, China
2
Ministry of Education Data China “Hundred Universities Project” Base, China
3
College of Mechanical and Electrical Engineering, Ningde Normal University, China
4
College of Logistics Engineering, Shanghai Maritime University, China
Submission date: 2025-05-26
Final revision date: 2025-06-28
Acceptance date: 2025-09-20
Online publication date: 2025-09-26
Publication date: 2025-09-26
Corresponding author
Feng Li
Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts, China
Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts, China
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(2):211142
HIGHLIGHTS
- We establish a model-fault-feature transmission path to support the identification.
- The PIEN framework integrates a hybrid prediction network.
- We develop a first-principles residual generator that quantifies causal relationships.
KEYWORDS
TOPICS
ABSTRACT
Real-time fault diagnosis for autonomous underwater vehicles (AUVs) is crucial for ensuring overall system safety. As a critical component, the thruster operates is prone to failure under complex environments. The healthy status of underwater thrusters holds significant importance in enhancing the efficiency and safety reliability of AUVs. This study proposes a Physics-Informed Estimation Network (PIEN) to address thruster fault diagnosis. First, sensor data from the thruster are collected to establish corresponding fault mechanism models. The estimation network then constructs a predictive model to generate online residual data by physical parameters. Finally, a residual fault detector determines thruster malfunction status. The experimental validation with the "Haizhe" AUV dataset demonstrates PIEN's ability to quickly detect different thruster faults in prototypes. It also achieves better diagnostic performance than manually designed models.
ACKNOWLEDGEMENTS
This work was supported in part by the National Natural Science Foundation of China under Grant 62303305, 62303308, in part by the Ministry of Education Data China “Hundred Universities Project” Base under Grant HUP202502, open projects funded by Hubei Engineering Research Center for Intelligent Detection and Identification of Complex Parts under Grant IDICP-KF-2024-09, IDICP-KF-2024-14, and in part by Shanghai Sailing Program (Grant no. 24YF2716300).
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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