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Autonomous Underwater Vehicle Sensor Fault Detection Using Machine Learning and Mismatch Generators
https://doi.org/10.17586/0021-3454-2025-68-9-762-773
Abstract
The problem of fault detecting and localizing of inertial system sensors used by an autonomous underwater vehicle during navigation is considered. A comparative analysis of solutions based on various machine learning models is proposed. Directional mismatch signal generators are built on the basis of full-order observers to detect and localize failures of sensors used to measure the linear and angular velocity of an underwater vehicle model. The dynamics of the autonomous underwater vehicle model moving at a constant longitudinal velocity in a horizontal plane is considered. The conditions are formulated to ensure the correct detection of failures and the sensitivity of mismatch generators to the failure of an individual sensor. The functions used as features for the proposed machine learning methods are designed. The setup was performed and a comparative analysis of the effectiveness of various machine learning models in the task of diagnosing sensors are carried out. The results of computer modeling are presented, demonstrating the high accuracy of fault detection during data augmentation for training due to mismatch signals.
Keywords
autonomous underwater vehicle,
fault detection and localization,
full-order observer,
machine learning methods
Supporting agencies: The article was prepared with the financial support of the Ministry of Science and Higher Education of the Russian Federation, project No. FSER-2025-0002.
About the Authors
D. N. Bazylev
ITMO University
Russian Federation
Russian Federation
Dmitry N. Bazylev — PhD; Faculty of Control Systems and Robotics; Associate Professor
St. Petersburg
A. A. Margun
ITMO University
Russian Federation
Russian Federation
Alexey A. Margun — PhD; Faculty of Control Systems and Robotics; Associate Professor
St. Petersburg
D. A. Galkina
ITMO University
Russian Federation
Russian Federation
Daria A. Galkina — Post-Graduate Student; Faculty of Control Systems and Robotics
St. Petersburg
M. V. Lyahovsky
ITMO University
Russian Federation
Russian Federation
Maxim V. Lyahovsky — Post-Graduate Student; Faculty of Control Systems and Robotics
St. Petersburg
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Review
For citations:
Bazylev D.N., Margun A.A., Galkina D.A., Lyahovsky M.V. Autonomous Underwater Vehicle Sensor Fault Detection Using Machine Learning and Mismatch Generators. Journal of Instrument Engineering. 2025;68(9):762-773. (In Russ.) https://doi.org/10.17586/0021-3454-2025-68-9-762-773
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