An unmanned marine vehicle thruster fault diagnosis scheme based on OFNDA

Abed, Wathiq; Sharma, Sanjay; Sutton, Robert I.; Khan, Asiya

doi:10.1080/20464177.2016.1264106

Cited by 15 publications

(3 citation statements)

References 20 publications

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“…The primary objective of the main engine studies is to create and develop a model based on current data, to select Machine Learning (ML) algorithms, deep learning (DL), Prognostics and Health Management (PHM), Support Vector Machine (SVM), Neural Networks (NN), Bayesian Networks (BNs), Gaussian Processes (GPs), Gaussian Mixture Model (GMM), and ensemble methods, to develop and explain the most appropriate model for quick and accurate detection of malfunctions that may. 49–53 As a result, the purpose of this research is to serve as an example of a successful data-based decision support system.…”

Section: Resultsmentioning

confidence: 99%

A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems

Orhan

Celik

2023

Proceedings of the Institution of Mechanical Engineers, Part M:

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Fault detection and diagnostics (FDD) have great potential to enable safety, efficiency, and reliability measures of critical machinery systems. However, it is clear that there is a lack of systematic literature review to identify and classify the FDD studies conducted within the scope of marine engineering. This paper offers a systematic review of FDD models particular to marine machinery and systems. The numbers of 72 core articles were highlighted through a comprehensive literature review conducted in the 2002–2022 period. The studies are classified based on the mostly utilized methods such as data-driven, model-based, knowledge-based, and new generation-hybrid. In addition, new generation and hybrid methods are discussed in detail. The experimental environment (i.e. shipboard, labs, simulator) and technical details of the conducted studies are extensively discussed. While 56.94% of the examined studies are related to the main engine, 43.06% of them are related to auxiliary engines. In addition, the main and auxiliary engine studies are also divided into subject headings and examined in detail. Given the recent developments in green and smart maritime concepts, a future research agenda of the FDD studies on marine machinery systems is then pinpointed. Consequently, the study stimulates scholars interested in FDD while it enables innovative ideas for marine engineers, technology providers, ship operators, and maritime entrepreneurs.

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Section: Resultsmentioning

confidence: 99%

A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems

Orhan

Celik

2023

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…9,10 At present, thruster fault diagnosis approaches generally consist of two main stages: the feature extraction and the fault identification. 11,12…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis approach for autonomous underwater vehicle thrusters using time-frequency entropy enhancement and boundary constraint–assisted relative gray relational grade

Yin

Zhang

Lin

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a novel thruster fault diagnosis approach for an autonomous underwater vehicle. In the novel approach, a time-frequency entropy enhancement is used to extract feature, and then a boundary constraint–assisted relative gray relational grade is applied to identify thruster fault. The time-frequency entropy enhancement is developed from the smoothed pseudo Wigner–Ville distribution combined with Shannon entropy. First, the energy distributions of autonomous underwater vehicle dynamic signals are given in the time-frequency plane. And then the energy concentration in the energy distribution is enhanced based on a serial signal processing, including wavelet decomposition, modified Bayes’ classification algorithm, and two dimensional convolution operation, successively. After that the Shannon entropy of the energy distribution is calculated. The boundary constraint–assisted relative gray relational grade comes from the gray relational analysis. A mapping function between the relative gray relational grade and the fault severity is established. And then the boundary constraints of relative gray relational grades at each standard fault level are determined. Moreover, the mapping function is modified based on the boundary constraints. Experiments are performed on an experimental prototype autonomous underwater vehicle in a pool. The experimental results demonstrate the effectiveness of the developed approaches in terms of improving the sensitivity of the fault feature to the fault severity, compared with the smoothed pseudo Wigner–Ville distribution combined with Shannon entropy, and increasing the identification accuracy, compared with the gray relational analysis.

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“…They extract fault feature firstly. And then they use the fault feature to train classifier and take the classifier to identify fault severity [13,14]. In terms of fault feature extraction, the wavelet coefficient modulus maximum method extracts fault feature from surge speed signal [15].…”

Section: Introductionmentioning

confidence: 99%

Thruster Fault Identification for Autonomous Underwater Vehicle Based on Time-Domain Energy and Time-Frequency Entropy of Fusion Signal

Yin

Lin

Tang

et al. 2019

Intelligent Robotics and Applications

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In order to deal with the problem of low classification accuracy in thruster fault identification based on single fault feature, a fault identification algorithm based on time-domain energy and time-frequency entropy of fusion signal is proposed. Firstly, the fault singular signals from two single aspects, such as surge speed and control voltage, are fused into a fusion signal to reflect fault information more comprehensively. Then, the peak region energy feature of the fusion signal is extracted in time domain, and the entropy feature of the fusion signal is extracted in time-frequency domain, so as to obtain the multidomain fault features. Finally, based on support vector data description algorithm, a multi-classifier is established, and the relative distance between fault sample and each hypersphere in the multi-classifier is calculated. The fault severity corresponding to the fault sample is determined by the minimum relative distance. The experimental results of an experimental prototype in a pool show that the classification accuracy of the proposed method is 95.2%. In comparison to the classification models corresponding to the surge speed and control voltage, the classification accuracy is increased by 5.2% and 22.8% respectively.

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An unmanned marine vehicle thruster fault diagnosis scheme based on OFNDA

Cited by 15 publications

References 20 publications

A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems

A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems

A fault diagnosis approach for autonomous underwater vehicle thrusters using time-frequency entropy enhancement and boundary constraint–assisted relative gray relational grade

Thruster Fault Identification for Autonomous Underwater Vehicle Based on Time-Domain Energy and Time-Frequency Entropy of Fusion Signal

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