Design of Underwater Thruster Fault Detection Model Based on Vibration Sensor Data: Generative Adversarial Network-based Fault Data Expansion Approach for Data Imbalance

Kim, Myung-Jun; Cho, Hyun-Joon; Choo, Ki-Beom; Huang, Jiafeng; Jung, Dong Hyun; Park, Jung-Hyeun; Lee, Jihyeong; Jeong, Sang-Ki; Ji, Dae-Hyeong; Choi, Hyeung-Sik

doi:10.18494/sam3991

Cited by 3 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results show that the proposed multi-signal approach achieves excellent thruster fault diagnosis results. Kim et al [15] proposed a fault detection system in which new vibration data were generated using a generative adversarial network (GAN) and was applied to a long short-term memory neural network. In the fault detection experiments of the underwater thruster, the vibration characteristics of the vibration sensor data obtained from the experiments and the data generated by the GAN were compared and analyzed using the fast Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for the Autonomous Underwater Vehicle via Meta-Self-Attention Multi-Scale CNN

Chen

Wang

et al. 2023

JMSE

View full text Add to dashboard Cite

Autonomous underwater vehicles (AUVs) are an important equipment for ocean investigation. Actuator fault diagnosis is essential to ensure the sailing safety of AUVs. However, the lack of failure data for training due to unknown ocean environments and unpredictable failure occurrences is challenging for fault diagnosis. In this paper, a meta-self-attention multi-scale convolution neural network (MSAMS–CNN) is proposed for the actuator fault diagnosis of AUVs. Specifically, a two-dimensional spectrogram of the vibration signals obtained by a vibration sensor is used as the neural network’s inputs. The diagnostic model is fitted by executing a subtask-based gradient optimization procedure to generate more general degradation knowledge. A self-attentive multi-scale feature extraction approach is used to utilize both global and local features for learning important parameters autonomously. In addition, a meta-learning method is utilized to train the diagnostic model without a large amount of labeled data, which enhances the generalization ability and allows for cross-task training. Experimental studies with real AUV data collected by vibration sensors are conducted to validate the effectiveness of the MSAMS–CNN. The results show that the proposed method can diagnose the rudder and thruster faults of AUVs in the cases of few-shot diagnosis.

show abstract

Section: Introductionmentioning

confidence: 99%