Recent advancements in data-driven methodologies for the fault diagnosis and prognosis of marine systems: A systematic review

Velasco-Gallego, Christian; Maya, Beatriz Navas de; Molina, Clara Matutano; Lazakis, Iraklis; Mateo, Nieves

doi:10.1016/j.oceaneng.2023.115277

Cited by 13 publications

(2 citation statements)

References 66 publications

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“…Also, periodic maintenance, overhauls, and regulations of necessary replacement parts on board enable a ship to maintain long-term operation and maintenance [ 18 ]. However, most Unmanned Surface Vehicles (USVs) follow scheduled maintenance and reactive strategies, and most fault-related research is heavily focused on commercial-class surface vessels [ 19 , 20 , 21 , 22 ].…”

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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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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“…The diagnosis of faults is crucial for ensuring the reliability and safety of system or equipment operation because it involves making precise judgments and analyzing the operational status and abnormal conditions. This process forms the basis for recovering the faults in the system [5]. Rapid advancements in sensing technology and artificial intelligence have facilitated the emergence of knowledge and data-driven intelligent fault diagnosis techniques, attracting considerable attention [6,7].…”

Section: Introductionmentioning

confidence: 99%

Research on a Fault Diagnosis Method for the Braking Control System of an Electric Multiple Unit Based on Deep Learning Integration

Wang,

Lin,

et al. 2024

Machines

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A fault diagnosis method based on deep learning integration is proposed focusing on fault text data to effectively improve the efficiency of fault repair and the accuracy of fault localization in the braking control system of an electric multiple unit (EMU). First, the Borderline-SMOTE algorithm is employed to synthesize minority class samples at the boundary, addressing the data imbalance and optimizing the distribution of data within the fault text. Then, a multi-dimensional word representation is generated using the multi-layer bidirectional transformer architecture from the pre-training model, BERT. Next, BiLSTM captures bidirectional context semantics and, in combination with the attention mechanism, highlights key fault information. Finally, the LightGBM classifier is employed to reduce model complexity, enhance analysis efficiency, and increase the practicality of the method in engineering applications. An experimental analysis of fault data from the braking control system of the EMU indicates that the deep learning integration method can further improve diagnostic performance.

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Fault diagnosis of marine machinery via an intelligent data-driven framework

Xu,

Lin,

2023

Ocean Engineering

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Recent advancements in data-driven methodologies for the fault diagnosis and prognosis of marine systems: A systematic review

Cited by 13 publications

References 66 publications

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

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

Research on a Fault Diagnosis Method for the Braking Control System of an Electric Multiple Unit Based on Deep Learning Integration

Fault diagnosis of marine machinery via an intelligent data-driven framework

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