An integration fault detection method using stator voltage for marine current turbines

Xie, Tao; Li, Zhichao; Wang, Tianzhen; Shi, Ming; Wang, Yide

doi:10.1016/j.oceaneng.2021.108808

Cited by 18 publications

(13 citation statements)

References 20 publications

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“…Zhan et al presented the use of a multi-class SVM for the FD of marine diesel engine cylinder covers, based on vibration analysis and PCA [95]. PCA as a foremost step of integration fault detection method of marine current turbines was proposed by Xie et al [37]. The PCA-based approach was used for building on-board sensor classifiers for water contaminant detection [96].…”

Section: Data Driven-based Methodsmentioning

confidence: 99%

“…To improve the convergence speed of K-means in large-scale ship data, Shi et al and Yan et al proposed objective function and feature weighting to optimize it and apply the Fuzzy C-means clustering algorithm to process ship data [35,36]. Gaussian mixture model (GMM) algorithm, frequency sequence subtraction (FSS), and other methods were also applied to the selecting of features [9,37]. These feature selection methods can obtain the feature data set, remove the redundant data while retaining the characteristics of original MSAE data, and simplify the complexity of problem analysis;…”

Section: Feature Processingmentioning

confidence: 99%

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Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

et al. 2022

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Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

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Section: Data Driven-based Methodsmentioning

confidence: 99%

Section: Feature Processingmentioning

confidence: 99%

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

et al. 2022

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“…[5][6][7] The functioning of marine power generation units often takes place under harsh conditions, which results into turbine or generator failures. [8][9][10][11] Faults can emerge either at the mechanical or at the electrical part of marine power generators and may result into power shortages, costly restoration procedures or even complete damage of equipment. [12][13][14] Consequently, early fault diagnosis in marine-turbine (MT) power generators is important for avoiding critical conditions and for assuring the safe functioning of these power generation units and uninterrupted power supply.…”

Section: Introductionmentioning

confidence: 99%

Flatness-based disturbance observer for condition monitoring of marine power generation units

Rigatos

Hamida

Abbaszadeh

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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The article proposes a flatness-based disturbance observer for condition monitoring of the integrated power generation system, which comprises a marine turbine and a synchronous generator. It is proven that this power generation system is differentially flat, thus it can be transformed into an input–output linearized form and into the canonical Brunovsky form. For the linearized state-space description of the system, state estimation can be performed with the use of Kalman Filtering (derivative-free nonlinear Kalman filter). Moreover, by re-designing the Kalman filter as a disturbance observer, it becomes possible to estimate disturbances, model uncertainty terms and fault-induced perturbations that affect the power unit. Consequently, model inconsistencies and faulty conditions in the functioning of the marine power unit can be diagnosed.

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“…There are many advantages to extract the attachment feature based on the current signal, but it is tough to extract the blade attachment feature due to the influence of current speed. There are many feature extraction methods based on the frequency domain, such as attachment feature extraction based on frequency sequence subtraction (FSS) 15 and Hilbert transform to demodulate the current signal. 16 They have a good detection effect under constant current speed.…”

Section: Introductionmentioning

confidence: 99%

Blade attachment degree classification for marine current turbines using AGMF-DFA under instantaneous variable current speed

Yang

Xie

Wang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part M:

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Marine current turbine (MCT) blades are continuously exposed to seawater, making them extremely susceptible to marine biological attachment and causing MCT blade imbalance fault. It is challenging to extract attachment features with the instantaneous variable current speed. In fact, the collected signal is highly nonlinear and nonstationary, which involves different scales and dimensions affected by the instantaneous current speed. In this paper, improved multifractal detrended fluctuation analysis (MF-DFA) is adopted to analyze such signals. The original MF-DFA is easily affected by the order of the fitting polynomial and cannot achieve the desired adaptive effect. The paper combines grey relation analysis and ensemble empirical mode decomposition (EEMD) to adaptively select the detrended function. The proposed adaptive grey MF-DFA (AGMF-DFA) method is validated with data collected from a testbed composed of an MCT coupled to a 230W permanent magnet synchronous generator. The result shows that the proposed method is effective for the attachment degree classification under the instantaneous variable current speed.

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An integration fault detection method using stator voltage for marine current turbines

Cited by 18 publications

References 20 publications

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

Marine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy

Flatness-based disturbance observer for condition monitoring of marine power generation units

Blade attachment degree classification for marine current turbines using AGMF-DFA under instantaneous variable current speed

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