Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear

Kong, Yun; Wang, Tianyang; Chu, Fulei

doi:10.1016/j.renene.2018.09.027

Cited by 95 publications

(38 citation statements)

References 45 publications

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“…At present, monitoring and fault diagnosis methods are mainly used in wind turbine gearboxes and other major components, such as wavelet-based approaches, statistical analysis, machine learning, as well as some other hybrid and modern techniques [4][5][6][7][8]. However, the need for transformation leads to extended detection time and the selection of mother wavelet remains a challenge for fault feature extraction of wind turbines gearboxes.…”

Section: Introductionmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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It is widely accepted that conventional boost algorithms are of low efficiency and accuracy in dealing with big data collected from wind turbine operations. To address this issue, this paper is devoted to the application of an adaptive LightGBM method for wind turbine fault detections. To this end, the realization of feature selection for fault detection is firstly achieved by utilizing the maximum information coefficient to analyze the correlation among features in supervisory control and data acquisition (SCADA) of wind turbines. After that, a performance evaluation criterion is proposed for the improved LightGBM model to support fault detections. In this scheme, by embedding the confusion matrix as a performance indicator, an improved LightGBM fault detection approach is then developed. Based on the adaptive LightGBM fault detection model, a fault detection strategy for wind turbine gearboxes is investigated. To demonstrate the applications of the proposed algorithms and methods, a case study with a three-year SCADA dataset obtained from a wind farm sited in Southern China is conducted. Results indicate that the proposed approaches established a fault detection framework of wind turbine systems with either lower false alarm rate or lower missing detection rate.

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Section: Introductionmentioning

confidence: 99%

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

et al. 2020

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“…However, they are also sensitive to noise. Some recently proposed signal decomposition algorithms such as variational mode decomposition (VMD) [ 26 ], morphological component analysis (MCA) [ 27 ], and empirical wavelet transform (EWT) [ 28 ] make great breakthroughs in overcoming the modal aliasing problem compared with those signal separation algorithms based on extreme point fitting. However, they are highly sensitive to parameter selection and are not conducive to online analysis.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Tang

Pang

et al. 2019

Entropy

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The accurate fault diagnosis of gearboxes is of great significance for ensuring safe and efficient operation of rotating machinery. This paper develops a novel fault diagnosis method based on hierarchical instantaneous energy density dispersion entropy (HIEDDE) and dynamic time warping (DTW). Specifically, the instantaneous energy density (IED) analysis based on singular spectrum decomposition (SSD) and Hilbert transform (HT) is first applied to the vibration signal of gearbox to acquire the IED signal, which is designed to reinforce the fault feature of the signal. Then, the hierarchical dispersion entropy (HDE) algorithm developed in this paper is used to quantify the complexity of the IED signal to obtain the HIEDDE as fault features. Finally, the DTW algorithm is employed to recognize the fault types automatically. The validity of the two parts that make up the HIEDDE algorithm, i.e., the IED analysis for fault features enhancement and the HDE algorithm for quantifying the information of signals, is numerically verified. The proposed method recognizes the fault patterns of the experimental data of gearbox accurately and exhibits advantages over the existing methods such as multi-scale dispersion entropy (MDE) and refined composite MDE (RCMDE).

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“…Combining advantages of both WT and EMD, the EWT can construct the wavelet basis in an adaptive way and decompose a signal according to the contained information. Therefore, EWT has attracted much attention and been used in a variety of applications, including medicine, biology [21,22], and machinery [23][24][25][26][27][28][29][30][31][32][33]. The traditional spectrum segmentation is achieved by detecting local maxima or minima of the spectrum.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

Ming

Zhang

et al. 2019

Applied Sciences

Self Cite

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In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.

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Meshing frequency modulation assisted empirical wavelet transform for fault diagnosis of wind turbine planetary ring gear

Cited by 95 publications

References 45 publications

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes

Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

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