Imbalance Fault Detection Based on the Integrated Analysis Strategy for Marine Current Turbines under Variable Current Speed

Xie, Tao; Wang, Tianzhen; Razik, Hubert

doi:10.3390/e22101069

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…(4) The IMFs and residual components are averaged to minimize the added white noise impact in ( 16) and (17). The final result of the EEMD is shown as (18).…”

Section: Detrended Function Calculationmentioning

confidence: 99%

“…where J m is the moment of inertia. According to the relationship between the generator stator current frequency and rotor speed under blade attachment state, 17 the stator current i s ( t ) can be expressed as…”

Section: Mct Attachment Description Under Instantaneous Variable Curr...mentioning

confidence: 99%

“…The extraction method of blade attachment feature based on adaptive proportional sampling frequency (APSF) can solve the influence of variable current speed and make good detection. 17 In order to further classify the attachment degree, a method based on variational modal decomposition (VMD) and S-LDA is proposed in, 18 but its classification accuracy is low, and a lot of data preprocessing is needed. To improve the accuracy of blade attachment classification, a method based on the wavelet coefficients in the shaft rotating frequency range of the MCT rotor shaft generated by the continuous market wavelet transform comprises a six-dimensional feature space.…”

Section: Introductionmentioning

confidence: 99%

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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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“…(4) The IMFs and residual components are averaged to minimize the added white noise impact in ( 16) and (17). The final result of the EEMD is shown as (18).…”

Section: Detrended Function Calculationmentioning

confidence: 99%

Section: Mct Attachment Description Under Instantaneous Variable Curr...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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:

Self Cite

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show abstract

“…As measured rotating-machinery signals are often nonstationary and can be highly noisy, there is a clear need for increasingly efficient signal-processing techniques to improve failure-diagnosis accuracy [32,33]. In this context, LPC, widely used especially in speech recognition for signal analysis and feature extraction, is an interesting option for investigating failure diagnosis signal processing.…”

Section: Linear Prediction Coefficientsmentioning

confidence: 99%

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Habbouche

Benkedjouh

Benbouzid

2021

Entropy

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Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

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“…In addition to the decomposition method, the resampling method is also used to deal with non-stationary conditions. In [37], a new adaptive proportional sampling frequency (APSF) is applied to transform the variable fault features into fixed ones. However, although the methods mentioned above can detect imbalance faults based on electrical signals, they cannot be used to make fault classifications for MCTs.…”

Section: Introductionmentioning

confidence: 99%

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

Wei

Xie

Shi

et al. 2021

JMSE

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Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

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Imbalance Fault Detection Based on the Integrated Analysis Strategy for Marine Current Turbines under Variable Current Speed

Cited by 12 publications

References 27 publications

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

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

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

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