Wind Turbine Bearing Fault Diagnosis Research Articles

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

This study presents a digital twin (DT) based wind turbine bearing fault diagnosis approach to address the issues of insufficient fault sample size and inaccurate diagnosis. To assist in diagnosing bearing faults in wind turbines, a DT system was built. Bearing vibration signal enhancement processing, which is based on the Hilbert-Huang transform, is used to improve the data samples of vibration signals and decrease the noise in these signals. In order to diagnose bearing defects in wind turbines, a convolutional neural network model was trained and tested using data-enhanced samples. The experimental results showed that the suggested method is feasible and effective, increased the stability and accuracy of defect diagnosis in wind turbine bearings, and solved the problem of data augmentation in one-dimensional vibration signals.

A novel method of wind turbine bearing fault diagnosis based on gradient projection sparse reconstruction and fractional Fourier transform and improved convolutional long short-term memory

The bearing is one of the most critical components of a wind turbine, and its condition determines whether the wind turbine can work normally or not. And in recent years, the increasing number of wind turbines leads to more and more data collected for bearing condition monitoring, which brings a great challenge to data transmission and preservation. In addition, the vibration signal is one-dimensional, and directly using one-dimensional signals can avoid the complex calculations caused by increasing the dimensionality of the data. In this article, a new fault diagnosis method for wind turbine bearings is proposed using one-dimensional data as input. The method is based on sparsely reconstructed gradient projection and fractional Fourier transform (GPSR-FRFT) for feature extraction of the signal, and a modified convolutional long short-term memory (CNN-LSTM) is used for fault classification of the data. Firstly, the signal is sparsely expressed by the GPSR algorithm, and the original signal is compressed and observed using compressed sensing so that the observed signal can be transmitted instead of the original signal, this process can reduce data samples to save storage space. The original signal is recovered by the inverse solution of compressed sensing at the terminal, and this step can reduce noise interference. Secondly, the time-frequency characteristics of the signal are obtained using the FRFT transform. In addition, in order to solve the problem of insufficient learning ability and low accuracy of convolutional long short-term memory (CNN-LSTM), an improvement was made by adding a jump connection layer to the convolutional layer to improve the training speed and learning ability. The resulting signal is input to an improved CNN-LSTM network, and experimental analysis proves the superiority of the proposed method.

Wind turbine bearing fault diagnosis by maximum generalised Gaussian cyclostationarity blind deconvolution

This study aims to assess the performance of IGGCS/GGS as a criterion for blind deconvolution in the detection of wind turbine bearing faults. The challenges associated with diagnosing wind turbine bearing faults include two primary issues: (1) the theoretical fault frequencies or orders may not align with the actual fault frequencies or orders, and (2) the vibration signal deviates from a Gaussian distribution. Consequently, the application of the blind deconvolution criterion ICS2, as utilized in CYCBD, may encounter difficulties due to the aforementioned reasons. To address this, the criterion is modified from ICS2 to IGGCS/GGS, and a tolerance band is introduced during the generation of the weighting matrix. These modifications aim to mitigate the impact of the aforementioned challenges. The effectiveness of the modified blind deconvolution approach, referred to as CYCBDβ, is evaluated in this study by comparing its performance against that of CYCBD using two distinct datasets from 2.0 MW wind turbines.

A Review of Research on Wind Turbine Bearings’ Failure Analysis and Fault Diagnosis

Bearings are crucial components that decide whether or not a wind turbine can work smoothly and that have a significant impact on the transmission efficiency and stability of the entire wind turbine’s life. However, wind power equipment operates in complex environments and under complex working conditions over long time periods. Thus, it is extremely prone to bearing wear failures, and this can cause the whole generator set to fail to work smoothly. This paper takes wind turbine bearings as the research object and provides an overview and analysis for realizing fault warnings, avoiding bearing failure, and prolonging bearing life. Firstly, a study of the typical failure modes of wind turbine bearings was conducted to provide a comprehensive overview of the tribological problems and the effects of the bearings. Secondly, the failure characteristics and diagnosis procedure for wind power bearings were examined, as well as the mechanism and procedure for failure diagnosis being explored. Finally, we summarize the application of fault diagnosis methods based on spectrum analysis, wavelet analysis, and artificial intelligence in wind turbine bearing fault diagnosis. In addition, the directions and challenges of wind turbine bearing failure analysis and fault diagnosis research are discussed.

Research on the effect of wind turbine bearing fault diagnosis method based on multi-feature calculation and Bayesian optimized machine learning method

Research on the effect of wind turbine bearing fault diagnosis method based on multi-feature calculation and Bayesian optimized machine learning method

Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds

Recent years have seen the rapid development and marvelous achievement of deep learning-based fault diagnosis (FD) methods which assume that training data and testing data have the same distribution. However, in real FD of wind turbine bearing (WTB), the particularity of time-varying speeds makes a huge difference in the distribution of training data and testing data, greatly increasing the difficulty of FD. Accordingly, in this paper, a novel deep residual deformable subdomain adaptation framework is proposed for cross-domain failure diagnosis of WTB under time-varying speeds. In the proposed approach, the traditional residual network is improved by using a deformable convolution module to replace plain counterparts, which can make the feature representation of an object adapt its configuration and enhance the ability of the model to extract transferable features. Moreover, the popular FD model based on domain adversarial neural nets and global maximum mean discrepancy is improved by removing the adversarial training mechanism and employing a local maximum mean discrepancy to align the distributions of the identical fault type in different domains, making the diagnostic model simpler and more efficient. Two experimental cases under time-varying speeds are conducted to analyze the performance of the proposed approach and the results indicate that this method can utilize the knowledge in the source domain to diagnose the fault in the target domain. Compared with the existing methods, the diagnosis accuracy and efficiency are significantly improved, demonstrating its effectiveness and potential applications in fault transfer diagnosis of wind turbine bearing.

A wind turbine bearing fault diagnosis method based on fused depth features in time–frequency​ domain

Diagnosis of bearing faults has significant meaning to the maintenance of wind turbines in real industry. Well-performed bearing fault diagnosis generally requires effective features extracted from vibration signals. However, conventional methods have shortages at obtaining comprehensive information of vibration signals. Aiming at this problem, this paper proposes a feature extraction method for wind turbine bearing fault diagnosis. This method contains three useful steps to realize accuracy improvement. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to extract time-domain feature, meanwhile fast Fourier transform (FFT) is performed twice for deep frequency-domain features extraction. Secondly, the recursive feature elimination (RFE) combined with the chi-square test is utilized to select optimal feature subset from the obtained time–frequency​ features. Thirdly, several classifiers are modeled, fed with these optimal features, and used for fault diagnosis. The industrial data from Case Western Reserve University (CWRU) and Jiangxi wind farm are taken in the experimental analysis. Numerical results show that the proposed method is effective and applicable in real wind turbine rolling bearing fault diagnosis.

A Probabilistic Bayesian Parallel Deep Learning Framework for Wind Turbine Bearing Fault Diagnosis.

The technology of fault diagnosis helps improve the reliability of wind turbines. Difficulties in feature extraction and low confidence in diagnostic results are widespread in the process of deep learning-based fault diagnosis of wind turbine bearings. Therefore, a probabilistic Bayesian parallel deep learning (BayesianPDL) framework is proposed and then achieves fault classification. A parallel deep learning (PDL) framework is proposed to solve the problem of difficult feature extraction of bearing faults. Next, the weights and biases in the PDL framework are converted from deterministic values to probability distributions. In this way, an uncertainty-aware method is explored to achieve reliable machine fault diagnosis. Taking the fault signal of the gearbox output shaft bearing of a wind turbine generator in a wind farm as an example, the diagnostic accuracy of the proposed method can reach 99.14%, and the confidence in diagnostic results is higher than other comparison methods. Experimental results show that the BayesianPDL framework has unique advantages in the fault diagnosis of wind turbine bearings.

The Multiclass Fault Diagnosis of Wind Turbine Bearing Based on Multisource Signal Fusion and Deep Learning Generative Model

Low fault diagnosis accuracy in case of the insufficient and imbalanced samples is a major problem in the wind turbine fault diagnosis. The imbalance of samples refers to the large difference in the number of samples of different categories, or the lack of a certain fault sample, which requires good learning of the characteristics of a small number of samples. Sample generation in the deep learning generation model can effectively solve this problem. In this study, we proposed a novel multi-class wind turbine bearing fault diagnosis strategy based on the conditional variational generative adversarial network (CVAE-GAN) model combining multi-source signals fusion. This strategy converts multi-source one-dimensional vibration signals into two-dimensional signals, and the multi-source two-dimensional signals were fused by using wavelet transform. The CVAE-GAN model was developed by merging the variational auto-encoder (VAE) with the generative adversarial network (GAN). The VAE encoder was introduced as the front end of the GAN generator. The sample label was introduced as the model input to improve the model’s training efficiency. Finally, the sample set was used to train encoder, generator and discriminator in the CVAE-GAN model to supplement the number of the fault samples. In the classifier, the sample set is used to do experimental analysis under various sample circumstances. The results show that the proposed strategy can increase wind turbine bearing fault diagnostic accuracy in complex scenarios.

Fault diagnosis of wind turbine bearing based on CNN-XGBoost

When a wind turbine bearing fails, because the fault signal shows the characteristics of instability and non-linearity,it is difficult to extract the fault features artificially, resulting in a lower accuracy of the wind turbine bearing fault classification. In order to realize the automatic extraction of wind turbine bearing faults and improve the accuracy of fault classification, this paper proposes a wind turbine bearing fault identification method based on the combination of convolutional neural network and limit gradient lifting algorithm. First, convert the original bearing fault signal into a grayscale image. Then, the gray image is input to the convolutional neural network to automatically extract the feature of the fault. Finally, the limit gradient boosting algorithm optimized by the grid search algorithm is used as a classifier to classify the fault. The simulation results show that the method used in this paper can achieve a high rate of correctness of wind turbine bearing fault identification, indicating that this method is effective for wind turbine bearing fault diagnosis.

A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in high-frequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

Wind Turbine Bearing Fault Diagnosis Based on Sparse Representation of Condition Monitoring Signals

An effective way to save the costs for data storage and transmission in bearing fault diagnosis of wind turbines is to use a sparse representation of massive condition monitoring signals. A proper dictionary that is adaptive to the analyzed signal containing time-varying components is a key issue to improve the sparse level in the sparse representation method, which has not been addressed in the literature. This paper explores a new sparse representation method that uses a new time-varying cosine-packet dictionary for the bearing fault diagnosis of wind turbines operating under varying speed conditions. First, the time-varying shaft rotating frequency (SRF) of a wind turbine is estimated from a generator current signal recorded synchronously with the vibration signal. Then, the new dictionary is designed, in which the basic functions, called time-varying cosine packet, change with the SRF. Finally, a sparse coefficient spectrum (SCS) of the vibration envelope signal is constructed by the sparse coefficients of the signal projection on the new dictionary. The merits of the proposed sparse representation method are that the dictionary designed is adaptive to the variations of major frequencies of the vibration signals, and the nonzero sparse coefficients over the dictionary designed have a clear physical meaning, i.e., representing the amplitude weights of the major order components contained in the vibration envelope signals. Therefore, the possible bearing fault characteristic orders can be identified from the SCS of the vibration envelope signal. Laboratory and field test results show that the sparse coefficients obtained by the new sparse representation method are suitable for the representations of the bearing vibration signals and the SCS is effective for bearing fault diagnosis of direct-drive wind turbines.

Fault diagnosis of wind bearing based on multi-scale wavelet kernel extreme learning machine

The principle of kernel Extreme Learning Machine (ELM) is demonstrated. On this basis, a multi - scale wavelet kernel extreme learning machine is proposed. The multi-scale wavelet kernel is used as the kernel function of the extreme learning machine. The test shows that it is an achievable extreme learning machine. Experiments show that, using the multi-scale wavelet kernel extreme learning machine in the wind turbine bearing fault diagnosis has higher classification accuracy and speed than the support vector machine classification algorithm, and has excellent application value.

Fault diagnosis of wind turbine bearing using wireless sensor networks

This paper proposes wireless sensor networks to monitor the condition of wind turbines. It addresses lifetime maximization issue of sensor nodes using stable election protocol for a cluster of upto nine wind turbines. This paper presents results of both experimental and simulation studies of a wind turbine plant, in which the vibration signals from each wind turbine are taken and with the help of machine learning technique, the fault diagnosis is done for a plant with wireless sensor networks. An experimental case study is performed from a wireless sensor networks with a well reported wind turbine bearing fault diagnosis data set. The outcome of the study shows that if the number of wind turbines is five for one base station, then the lifetime of the sensor nodes are maximum using MATLAB.

Fault diagnosis of wind turbine bearing based on variational mode decomposition and Teager energy operator

Vibration signal of wind turbine has the non-linear and non-stationary characteristic, thus it is difficult to extract the fault feature. In this study, a novel method based on variational mode decomposition (VMD) and Teager energy operator (TEO) is proposed to diagnose the bearing faults of wind turbine. First, vibration signal is decomposed into several intrinsic mode function (IMF) components by means of VMD, which is a recently proposed signal decomposition method. Then, the most sensitive IMF component is selected according to kurtosis criterion. Moreover, TEO is applied to the most sensitive IMF in order to highlight impact signal. Finally, spectrum is obtained by applying Fourier transform to Teager energy of the selected IMF, thus extracting the fault feature to diagnose bearing fault. The effectiveness of the proposed method for fault diagnosis is validated by simulation and experimental signal analysis results, and comparison studies show its advantage over empirical mode decomposition and conventional spectrum analysis for wind turbine bearing fault diagnosis.

Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropy

For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.

A novel wind turbine bearing fault diagnosis method based on Integral Extension LMD

This paper proposed a novel wind turbine ball bearing fault diagnosis method based on Integral Extension Local Mean Decomposition (IELMD). Wind turbine vibration signal has the characteristic of non-Gaussian and non-stationary. Some typical time–frequency analysis methods cannot achieve ideal effects. A new method named LMD can deal with non-stationary signals and can extract obvious features. The IELMD method is proposed based on the integral local waveform matching of the right and left side of the original signal, in order to suppress the end effect of LMD method itself. Firstly, all the extreme points are scanned out, in which the left three points and right three points are specially marked. Secondly, two characteristic waveforms and two matching waveforms are established at both the left and right side. Finally, both the left and right matching waveforms are extended to finish the LMD process. The wind turbine bearing fault diagnosis experimental can improve the efficiency and validity of the novel IELMD method.