An Enhanced Empirical Wavelet Transform for Features Extraction from Wind Turbine Condition Monitoring Signals

Pu, Shi; Yang, Wenxian; Sheng, Meiping; Wang, Minqing

doi:10.3390/en10070972

Cited by 25 publications

(13 citation statements)

References 22 publications

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“…In [17], a simplified nonlinear gear model is at first developed, on which a time-frequency method is applied for a first overview; subsequently, the case of varying loads is examined through Empirical Mode Decomposition (EMD), for decomposing the vibration signals into meaningful components associated with specific frequency bands. The EMD is employed also in [18], where an enhanced Empirical Wavelet Transform method is proposed and tested on open access data of wind turbine gears and bearings: the results support that the proposed method is effective for segmenting the frequency spectrum of the signal and for detecting the fault-related features. In [19], a critical analysis of the synchrosqueezing transform for the representation of non-stationary signals is proposed: for this aim, the synchrosqueezing transform is improved using iterative generalized demodulation and the proposed method is validated using both numerically simulated and experimental vibration signals of wind turbines planetary gearboxes.…”

Section: Introductionmentioning

confidence: 74%

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

et al. 2020

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Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.

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Section: Introductionmentioning

confidence: 74%

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

et al. 2020

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“…Moreover, the linearity of the wavelet filtering bandwidth makes it non-adaptive for all the cases. 10 Furthermore, many condition monitoring and fault diagnosis works preferred DWT as a signal decomposition tool, 11,12 where the analyzed data is decomposed with a band-pass filter in time and frequency domains into a collection of signals with a particular frequency band. 13 Unfortunately, the dyadic step in the down-sampling process seems to be the main limit of DWT.…”

Section: Introductionmentioning

confidence: 99%

Gearboxes fault detection under operation varying condition based on MODWPT, Ant colony optimization algorithm and Random Forest classifier

Ikhlef

Rahmoune

Bettahar

et al. 2021

Advances in Mechanical Engineering

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Gearboxes are massively utilized in nowadays industries due to their huge importance in power transmission; hence, their defects can heavily affect the machines performance. Therefore, many researchers are working on gearboxes fault detection and classification. However, most of the works are carried out under constant speed conditions, while gears usually operate under varying speed and torque conditions, making the task more challenging. In this paper, we propose a new method for gearboxes condition monitoring that is efficiently able to reveal the fault from the vibration signatures under varying operating condition. First, the vibration signal is processed with the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then the features set are reduced using the Ant colony optimization algorithm (ACO) by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm Random Forest (RF) is used to train a model able to classify the fault based on the selected features. The innovative aspect about this method is that, unlike other existing methods, ACO is able to optimize not only the features but also the parameters of the classifier in order to obtain the highest classification accuracy. The proposed method was tested on varying operating condition real dataset consisting of six different gearboxes. In the aim to prove the performance of our method, it had been compared to other conventional methods. The obtained results indicate its robustness, and its accuracy stability to handle the varying operating condition issue in gearboxes fault detection and classification with high efficiency.

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“…The effectiveness of the proposed method has been validated by analyzing two experimental cases. By developing a feasible and efficient spectrum segmentation method, Yang et al [37] also proposed an enhanced EWT and validated the effectiveness of this method by using the bearing and gearbox signals. In 2016, Lin et al [38] utilized EWT to decompose the acoustic emission (AE) signals to extract the bearing fault feature by calculating the correlated kurtosis (CK) of the mono-components.…”

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

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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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An Enhanced Empirical Wavelet Transform for Features Extraction from Wind Turbine Condition Monitoring Signals

Cited by 25 publications

References 22 publications

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

Gearboxes fault detection under operation varying condition based on MODWPT, Ant colony optimization algorithm and Random Forest classifier

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

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