An imbalance fault detection method based on data normalization and EMD for marine current turbines

Zhang, Milu; Wang, Tianzhen; Tang, Tianhao; Benbouzid, Mohamed; Diallo, Demba

doi:10.1016/j.isatra.2017.02.011

Cited by 68 publications

(41 citation statements)

References 39 publications

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“…In recent years, researchers have proposed some methods or strategies to reduce the interference and extract the imbalance fault signature. The methods proposed in [18] and [26] use the EMD to decompose the raw signal into IMFs. The EMD method can reduce the interference information, which is caused by environmental factors.…”

Section: Problem Descriptionmentioning

confidence: 99%

A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

Wang²,

Wang³

et al. 2020

IEEE Access

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Blade imbalance fault caused by the marine organisms is considered as the most important fault in marine current turbines. Therefore, it is important to detect the fault accurately and quickly to mitigate its effect, minimize the downtime, and maximize the productivity. Imbalance fault detection methods using generator stator current signals have attracted attentions due to their low cost, operability and stability compared to the ones using vibration analysis. However, it is difficult to extract the fault signature and automatically detect the imbalance fault under different flow velocity conditions. In this paper, a wavelet threshold denoising-based imbalance fault detection method using the stator current is proposed. The signal is analyzed through three consecutive steps: the parameters offline setting based on wavelet threshold denoising, the Hilbert transform method and the Principle Component Analysis-based detection method. With this approach, the imbalance fault can be detected automatically. The imbalance fault detection is assessed under different flow velocity conditions and validated using an experimental platform. The results are promising with false alarm and false negative rates less than 1% and 5% respectively when using Q statistic. Moreover, the experimental results show that the proposed method has good stability under different flow velocity conditions. INDEX TERMS Hilbert transform, imbalance fault, marine current turbine, principle component analysis, wavelet.

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Section: Problem Descriptionmentioning

confidence: 99%

A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

Wang²,

Wang³

et al. 2020

IEEE Access

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“…Alternatively, the S-transform is superior in the time-frequency analyses of non-stationary signals, which eliminates the limitations of STFT and WT [14]. Moreover, the empirical mode decomposition (EMD) method shows excellent performance in non-stationary signal processing due to its local adaptive feature [15,16]. Nevertheless, the EMD also has some limits in application due to its recursive calculation and mode-mixing problems [17,18].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

Zhu

et al. 2019

Energies

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Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

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“…Condition-based maintenance technology is a pivotal solution to reduce the long-term operation and maintenance cost of MCTs [1]. However, due to the complex marine environment, the condition-based maintenance of MCT faces the following problems: few monitoring variables, various fault features, and strong interferences [2]. In order to improve the safety and reliability of MCT, a fault detection strategy for non-stationary signals with strong interference is needed [3].…”

Section: Introductionmentioning

confidence: 99%

A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions

et al. 2019

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Affected by high density, non-uniform, and unstructured seawater environment, fault detection of Marine Current Turbine (MCT) faces various fault features and strong interferences. To solve these problems, a harmonic analysis strategy based on zero-crossing estimation and Empirical Mode Decomposition (EMD) filter banks is proposed. First, the detection problems of rotor imbalance fault under strong interference conditions are described through an analysis of the fault mechanism and operation environment of MCT. Therefore, against various fault features, a zero-crossing estimation is proposed to calculate instantaneous frequency. Last, and in order to solve the problem that the frequency and amplitude of the operating parameters are partially or completely covered by interference, a band-pass filter based on EMD is used, together with a characteristic frequency selected by a Pearson correlation coefficient. This strategy can accurately detect the multiplicative faults under strong interference conditions, and can be applied to the MCT fault detection system. Theoretical and experimental results verify the effectiveness of the proposed strategy.

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An imbalance fault detection method based on data normalization and EMD for marine current turbines

Cited by 68 publications

References 39 publications

A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

A Synchronous Sampling Based Harmonic Analysis Strategy for Marine Current Turbine Monitoring System under Strong Interference Conditions

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