Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Tsai, Chia-Ming; Wang, Chiao-Sheng; Chung, Yu-Jen; Sun, Yung-Da; Perng, Jau-Woei

doi:10.3390/s21217187

Cited by 19 publications

(7 citation statements)

References 41 publications

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“…Factors such as currents and pressure, as well as collisions with floating debris, can partially or totally damage underwater thrusters. This makes it more difficult to implement ROV motion, leading to mission failure or loss of the ROV [15,33]. This study focused on the most frequently occurring faults caused by external factors, such as entanglement with floating debris and propeller breakage [19].…”

Section: Thruster Faultsmentioning

confidence: 99%

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

Park,

Cho,

Gil

et al. 2024

Applied Sciences

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The objective of this study was to perform fault diagnosis (FD) specific to various faults that can occur in the thrusters of remotely operated vehicles (ROVs) during hovering control. Underwater thrusters are predominantly utilized as propulsion systems in the majority of ROVs and are essential components for implementing motions such as trajectory tracking and hovering. Faults in the underwater thrusters can limit the operational capabilities of ROVs, leading to permanent damage. Therefore, this study focused on the FD for faults frequently caused by external factors such as entanglement with floating debris and propeller breakage. For diagnosing faults, a data-based technique that identifies patterns according to data characteristics was utilized. In imitation of the fault situations, data for normal, breakage and entangled conditions were acquired, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was employed to differentiate between these fault conditions. The proposed methodology was validated by configuring an ROV and conducting experiments in an engineering water tank to verify the performance of the FD.

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Section: Thruster Faultsmentioning

confidence: 99%

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

Park,

Cho,

Gil

et al. 2024

Applied Sciences

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“…denote the matrices composed of query vector, key vector, and value vector, respectively. For the query vector q n ∈ Q, the output vector h n can be obtained by using the key-value pair attention mechanism of Equation (4).…”

Section: Fault Diagnosis Model With Self-attentive Multi-scale Featur...mentioning

confidence: 99%

“…The information that can be measured by the sensors inside the AUVs is used as an input parameter. Tsai et al [4] proposed a fault diagnosis method for underwater thrusters based on deep convolutional neural networks, where the raw data were transformed from the time domain to the frequency domain by fast Fourier transform and used as the input to the neural network. Chu et al [5] proposed an underwater thruster fault diagnosis method based on the random forest regression and support vector machine, which mainly solves the problem of insufficient diagnostic accuracy due to sample imbalance.…”

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

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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.

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“…To address the inherent challenges in fault diagnosis via hull vibrations and to diagnose faults at varying rotational speeds of USVs’ propulsion systems, the Continuous Wavelet Transform (CWT) was applied to convert time-series vibration data into scalograms [ 30 , 31 ]. Although a number of methodologies have been applied for fault diagnosis at a constant rotation speed of USV thrusters [ 32 , 33 , 34 ], analyzing varying rotational speeds demands a methodology that simultaneously accounts for attributes in both the temporal and frequency domains. CWT, renowned for its ability to encapsulate both the physical characteristics and time-frequency domain nuances, has been extensively researched and validated within the realm of Physics-Informed Neural Networks (PINNs) [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

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

Cho,

Park,

Choo

et al. 2024

Sensors

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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.

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Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Cited by 19 publications

References 41 publications

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

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

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

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