Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox

Inturi, Vamsi; Sabareesh, G. R.; Supradeepan, K.; Penumakala, Pavan Kumar

doi:10.1177/1077546319841495

Cited by 50 publications

(28 citation statements)

References 30 publications

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“…The purpose of blind deconvolution is to separate the input signal source s 0 from the collected signal x by constructing an inverse filter: s = x * h = (s 0 * g) * h ≈ s 0 (1) where g represents the frequency response function of an unknown system, h denotes the inverse filter, s refers to the estimated signal source and * indicates the convolution operation. The convolution operation for discrete signal can be described as the following matrix form:…”

Section: Theoretical Background Of Cyclostationary Blind Deconvolutionmentioning

confidence: 99%

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Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Wang

Yan

2019

Applied Sciences

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The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement.

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Section: Theoretical Background Of Cyclostationary Blind Deconvolutionmentioning

confidence: 99%

“…In the wind power field, without proper detection and maintenance, bearing flaw may lead to non-planned shutdown or even result in catastrophic accident. Therefore, the incipient fault detection of rolling bearing is of great significant to ensure stable operation of wind turbine [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Wang

Yan

2019

Applied Sciences

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“…Therefore, the high dimensional feature data set has been normalised and it is subjected to dimensionality reduction in order to reduce the computational time. Principal component analysis (PCA) is an unsupervised machine learning algorithm used for the dimensionality reduction [8]. PCA is a mathematical approach that performs the orthogonal linear transformation on the input data in such a way that, the data consists of greatest variance (principal component 1) comes to as the first coordinate, the next greatest variance (principal component 2) lie as the second coordinate, and so on.…”

Section: Modeling Of Health Indicatormentioning

confidence: 99%

PCA based health indicator for remaining useful life prediction of wind turbine gearbox

Praveen¹,

Shah²,

Pandey³

et al. 2019

Vib. proced.

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Fault prognosis of wind turbine gearbox has received considerable attention as it predicts the remaining useful life which further allows the scheduling of maintenance strategies. However, the studies related towards the RUL prediction of wind turbine gearbox are limited, because of the complexity of gearbox, acute changes in the operating conditions and non-linear nature of the acquired vibration signals. In this study, a health indicator is constructed in order to predict the remaining useful life of the wind turbine gearbox. Run to fail experiments are performed on a laboratory scaled wind turbine gearbox of overall gear ratio 1:100. Vibration signals are acquired and decomposed through continuous wavelet transform to obtain the wavelet coefficients. Various statistical features are computed from the wavelet coefficients which return form high-dimensional input feature set. Principal component analysis is performed to reduce the dimensionality and principal components (PCs) are computed from the input feature set. PC1 is considered as the health indicator and subjected to further smoothening by linear rectification technique. Exponential degradation model is fit to the considered health indicator and the model is able to predict the RUL of the gearbox with an error percentage of 2.73 %.

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“…There are many studies focused on condition monitoring and relevant technological innovations. As vibration analysis is still the most popular approach, many studies have contributed to signal-processing methods for condition monitoring, such as the short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) [10,11] and wavelet transform (WT) [12,13]. Alternatively, the S-transform is superior in the time-frequency analyses of non-stationary signals, which eliminates the limitations of STFT and WT [14].…”

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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Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox

Cited by 50 publications

References 30 publications

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

PCA based health indicator for remaining useful life prediction of wind turbine gearbox

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

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