Unmanned Surface Vehicle Thruster Fault Diagnosis via Vibration Signal Wavelet Transform and Vision Transformer under Varying Rotational Speed Conditions

Among unmanned surface vehicle (USV) components, underwater thrusters are pivotal in their mission execution integrity. Yet, these thrusters directly interact with marine environments, making them perpetually susceptible to malfunctions. To diagnose thruster faults, a non-invasive and cost-effective...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-03, Vol.24 (5), p.1697
Main Authors: Cho, Hyunjoon, Park, Jung-Hyeun, Choo, Ki-Beom, Kim, Myungjun, Ji, Dae-Hyeong, Choi, Hyeung-Sik
Format: Article
Language:English
Subjects:
Automation
Computational linguistics
Computer software industry
continuous wavelet transform
Design
Electric transformers
Fault diagnosis
Image processing
Language processing
Natural language interfaces
Neural networks
Radio communications
Sensors
transfer learning
underwater thruster
unmanned surface vehicle
Vehicles
Vibration
vibration signal
Wavelet transforms
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Summary:Among unmanned surface vehicle (USV) components, underwater thrusters are pivotal in their mission execution integrity. Yet, these thrusters directly interact with marine environments, making them perpetually susceptible to malfunctions. To diagnose thruster faults, a non-invasive and cost-effective vibration-based methodology that does not require altering existing systems is employed. However, the vibration data collected within the hull is influenced by propeller-fluid interactions, hull damping, and structural resonant frequencies, resulting in noise and unpredictability. Furthermore, to differentiate faults not only at fixed rotational speeds but also over the entire range of a thruster's rotational speeds, traditional frequency analysis based on the Fourier transform cannot be utilized. Hence, Continuous Wavelet Transform (CWT), known for attributions encapsulating physical characteristics in both time-frequency domain nuances, was applied to address these complications and transform vibration data into a scalogram. CWT results are diagnosed using a Vision Transformer (ViT) classifier known for its global context awareness in image processing. The effectiveness of this diagnosis approach was verified through experiments using a USV designed for field experiments. Seven cases with different fault types and severity were diagnosed and yielded average accuracy of 0.9855 and 0.9908 at different vibration points, respectively.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24051697