Underwater Thruster Fault Detection and Isolation

Kemp, Mathieu

doi:10.2514/6.2019-1959

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…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%

“…Data-based techniques classify normal and fault states by identifying patterns based on data characteristics using measured parameters, state variables, and residuals as fault features [18]. The objective is to identify faults caused by external factors-particularly entanglement with floating debris and propeller breakage, which are among the most frequently occurring [19]. According to previous FD studies, the consumption current and rotation speed (in revolutions per minute, RPM) of underwater thrusters were selected as fault features for FD under normal conditions, propeller breakage, and entanglement [20].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

Park,

Cho,

Gil

et al. 2024

Applied Sciences

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“…If the aerial underwater vehicle fuselage is symmetrical about each coordinate system, the inertia matrix of its movement in the water can be set to J w = diag J xxw J yyw J zzw . Similarly, the translational and rotational dynamics of the UAUV in water can be obtained by the Formulas (5), (15) and (18) as:…”

Section: Dynamics When H > εmentioning

confidence: 99%

System Modeling and Simulation of an Unmanned Aerial Underwater Vehicle

Chen

Liu

Meng

et al. 2019

JMSE

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Unmanned Aerial Underwater Vehicles (UAUVs) with multiple propellers can operate in two distinct mediums, air and underwater, and the system modeling of the autonomous vehicles is a key issue to adapt to these different external environments. In this paper, only a single set of aerial rotors with switching propulsion abilities are designed as driving components, and then a compound multi-model method is investigated to achieve good performance of the cross-medium motion. Furthermore, some additional variables, such as water resistance, buoyancy and their corresponding moments are considered for the underwater case. In particular, a critical coefficient for air-to-water switching is presented to express these gradually changing additional variables in the cross-medium motion process. Finally, the sliding mode control method is used to reduce the altitude error and attitude error of the vehicles with external environmental disturbances. The proposed scheme is tested and the model is verified on the simulation platform.

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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%

“…The experimentation was conducted at sea, encompassing the collection of both normal status data and data under simulated fault cases. Common fault scenarios that are attributed to external factors were selected as fault cases [ 32 , 37 , 38 , 39 ]. The experimental results of this study aim to demonstrate the proposed method’s effectiveness as a non-invasive technique capable of not only identifying the type but also the extent of the faults.…”

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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Underwater Thruster Fault Detection and Isolation

Cited by 6 publications

References 8 publications

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

Research on Clustering-Based Fault Diagnosis during ROV Hovering Control

System Modeling and Simulation of an Unmanned Aerial Underwater Vehicle

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

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