Artificial neural network (ANN) (Agatonovic-Kustrin et al., 2000; Xi et al., 2020) is one of the most frequently used supervised learning algorithms. ANN consists of numerous neurons and is divided into input layer, hidden layer, and output layer. ANN is widely used in the fault diagnosis field (Samanta et al., 2003; Saravanan and Ramachandran, 2010). By learning from known fault samples, the mapping relationship between fault characteristics and fault categories is established to detect whether a device is faulty. Figure 5 shows a three-layer simple WT fault diagnosis model based on ANN, x 1 , x 2 , … , x n are the input characteristics of WT, n is the total sample of input characteristics, and m is the total fault types of WT.

The frequently employed neural network methods include adaptive resonance theory (ART), self-organizing map (SOM) neural network and radial basis function (RBF) neural network.

Zhang et al. (Zhang and Wang, 2014). proposed an ANN-based fault diagnosis method for the WT main bearing based on the WT SCADA system data. The difference between the theoretical and the actual parameter values can identify the early stage of the main bearing faults of WT. To decrease the time of ANN for WT fault detection, Bielecki et al. (2014) proposed a hybrid method of ART and RBF neural networks for online detection of the operating status of WT, which can monitor the status of WT in time, identify the early fault conditions and have good real-time performance. However, the actual engineering in a wind farm cannot collect all information on the fault, and the ANN cannot make accurate fault diagnosis. Therefore, Zhao (Zhao et al., 2015) proposed to apply the SOM neural network to the fault diagnosis of WT and to train the network through the sample data of the normal WT state. This is judged whether the wind turbine malfunctions according to the position of the output neuron in the output layer. Accordingly, the SOM neural network method can effectively diagnose the WT fault with good robustness.

Although the fault diagnosis of ANN has high precision and good robustness, this method requires numerous parameters for modeling, and the training model takes a long time. China’s wind power industry started late, but WT fault diagnosis research has been developed in the recent years. However, WT fault data samples are considerably lacking, and the accuracy and completeness of the WT data samples directly affect the accuracy of fault diagnosis classification. This issue is currently the main drawback restricting the development of ANN in WT fault diagnosis.

Support vector machine (SVM) is a kernel-based ML method used in regression problems and classification tasks introduced by Vapnik (2013). The main idea is to find two parallel hyperplanes to separate two sets of data in a multi-dimensional space and maximize the margin between the hyperplanes. SVM formulation ensures that the decision hyperplane is constructed with structural risk minimization to obtain a balance between empirical risk and complexity of model (Deka, 2014). SVM is mainly used in nonlinear problems, by building a classification hyperplane as a decision plane, in which the isolation boundry between negative and positive samples is maximized. As shown in Figure 6, any hyperplane can be represented by a normal vector W and a constant b (intercept) as follows: w T x + b = 1 (3) w T x + b = 0 (4) w T x + b = − 1 (5) For point A ( x 1 , y 1 ) , any two hyperplanes have a geometric interval d.

SVM is to find a hyperplane to make the data points separable, in which the minimum geometric distance is the largest. The SVM solution process can be regarded as the solution process of a convex quadratic problem, which has a global optimal solution. Thus, SVM is widely used in the fault diagnosis field.

To solve the local optimal phenomenon caused by the improper selection of sample parameters, Laouti et al. (2011) chose a radial basis function as the kernel parameter of SVM, which can immediately detect the WT blade pitch positionand generator failureand has good generalization performance. To further solve the problem of overfitting or underfitting caused by the improper selection of nuclear parameters, Tang et al. (2014) proposed a method of WT fault diagnosis based on the Shannon wavelet SVM (SWSVM) and manifold learning. In this method, mixed-domain features are extracted to construct a high-dimensional feature set, manifold learning is used to compress the high-dimensional feature set into low-dimensional eigenvectors, and low-dimensional eigenvectors are inputted into an SWSVM to recognize WT gearbox faults. Gao et al. (2018) proposed a novel fault diagnosis method of WT that combines mean decomposition, multi-scale entropy, least squares, and SVM. In this method, the WT raw vibration signal is divided into several groups for preprocessing. Thereafter, the mean decomposition method is applied to group the signals to obtain the product function. Moreover, the feature parameters are obtained using the multi-scale entropy method of processing the main product function to obtain the feature vector. The characteristic parameters were input into the least squares SVM, which was trained. This method can significantly enhance the fault classification ability of a single SVM and classify the fault type precisely. In the case of single kernel parameters and parameter optimization, Zhao et al. (2018) proposed a fault diagnosis method of WT based on random subspace identification and multi-kernel SVM. Compared with the traditional SVM, the multi-kernel SVM can successfully identify the bearing fault of the WT and has higher fault diagnosis accuracy. In the classification problem, there are not only two classification problems, but also multi classification problems. SVM can also show good classification ability in the face of two classification problems. (Liu K. et al., 2020) used multi-SVM machine to diagnose the fault of renewable energy power grid, which effectively improves the accuracy of fault diagnosis. (Xue et al., 2017). proposed a fault intelligent diagnosis method combining optimal composition of symptom parameters (SPOC) and multi-SVM to diagnose the motor fault, and realized the fault detection and identification of multiple motor faults. In recent years, with the wide application of SVM, experts began to optimize and improve SVM, put forward some machine learning algorithms derived from SVM, put them into the field of fault diagnosis, and achieved good results. (Zhang and Zhou, 2014; Tang et al., 2019).introduced margin mean and margin variance on the basis of SVM and proposed a large margin distributed machine (LDM), and this method has better classification performance than SVM. (Tang et al., 2020a) used LDM to detect the fault of WT’s pitch system and optimized it with state transition algorithm (STA), which significantly improved the accuracy of fault detection.

SVM uses inner product kernel function to turn the raw data into linear data through mapping the raw data to a high-dimensional space. However, modeling WT big data is difficult, and the selection of kernel parameters also affects the fault diagnosis accuracy. Moreover, guaranteeing the classification of multi-type WT fault problems is difficult.

Decision tree (DT) is composed of multiple judgment nodes, and a classification or regression model is formed by the tree structure (Safavian and Landgrebe, 1991). The basic idea is simple, and Figure 7 shows a WT fault diagnosis model based on DT.

Rabah et al. (Benkercha and Moulahoum, 2018) proposed a fault diagnosis method for a grid-connected WT generator system based on the DT algorithm with high prediction performance and high accuracy. Abdallah et al. (2018) adopted the DT algorithm to perform fault diagnosis on WT, continuously sampled extensive data from thousands of WT at a high rate, and trained integrated DT classifier. Compared with other ML algorithms, DT is easy to implement but it has limitations in dealing with missing values. The WT fault diagnosis process, there are few samples of fault type and more samples of fault-free type. For DT that deals with data with inconsistent sample sizes in various categories, information gain is biased toward features with additional numerical values, which is easy to overfit and minimally used in WT fault diagnosis.

The basic concept of ensemble learning (Polikar, 2012; Liu et al., 2019) is to adjust and train multiple base learners as ensemble members into a strong learner that should have greater performance on average than any other ensemble member. Thereafter, a model is estabilshed by optimizing the loss function to advance the performance of fault classification. The frequently employed ensemble learning methods include bagging, boosting.

Bootstrap aggregating, also called bagging (Breiman, 1996) applied in regression and statistical classification, is an ML ensemble that obtains a new data set by returning the samples, trains a better base learner based on each new data set, and eventually combines the base learners. The algorithm reduces variance and helps to prevent overfitting. Typical bagging algorithm including random forest (RF). A diagnosis method (Cabrera et al., 2015) was presented for detecting the faults of WT gearboxes, which is based on Random Forest. First, the condition parameters of the vibration signal are extracted by wavelet packet decomposition and used as the input feature of the classification problem. Second, a study approximates the parameter space to find the best mother wave set, and select the best feature through the internal ranking of the random forest classifier. Lastly, the RF algorithm is used to detect the fault of the WT gearbox. To further improve the fault detection rate, Li et al. (2016) proposed a method based on deep RF fusion (DRFF) to improve the fault detection performance of the WT gearboxes. Two deep Boltzmann machines are used to characterize the parameter values of the wavelet packet transform, and the output of the two deep Boltzmann machines is fused into an integrated DRFF model using an RF algorithm. The results indicate that DRFF may improve fault diagnosis capabilities for gearboxes compared with conventional RF.

Boosting (Freund and Schapire, 1996) adjusts the algorithm by giving considerable importance to the bad classification that results in significant improvements in performance of classification. The bagging algorithm focuses on reducing bias facilitates prevention of overfitting. Many algorithms are based on boosting methods, such as XGBoost and LightGBM. Zhang et al. (2018) proposed an efficient WT fault detection method based on the RF and XGBoost. RF is used to rank the features by importance, while XGBoost trains the ensemble classifier for each specific fault based on the top-ranking features. The proposed ensemble classifier can protect against overfitting and experiments verifies the robustness of this method. To enhance the fault diagnosis accuracy, Tang et al. (2020b) proposed the adaptive LightGBM method for the WT gearbox fault detection. The correlation of the WT data samples is analyzed using the maximum information coefficient to realize the feature selection of fault detection. Meanwhile, the LightGBM method after Bayesian hyperparameter optimization is used for the fault detection of WT gearbox. Experiments prove that this method has a low false alarm rate and missing detection rates.

Ensemble learning is widely used in fault diagnosis and early warning of WT with high accuracy. However, some algorithms have slow convergence speed, weak learners rely heavily on one another, and over-fitting problems occur. When using the ensemble methods, the number of iterations, number of base learners, and weights are the issues that should be considered.

Deep learning was proposed by Hinton et al. (LeCun et al., 2015), and the basic idea is a ML process that includes a multi-level deep network structure through a certain training method based on sample data. Deep learning combines low level features to form a considerably abstract high-level representation to discover the distributed feature representations of data. Deep learning (Schmidhuber, 2015; Goodfellow et al., 2016) is widely used in image processing, data mining, fault diagnosis (Helbing and Ritter, 2018), and other fields. Different deep learning (Jiang et al., 2018) configurations have also been introduced such as deep belief nets (DBNs), deep auto-encoder (DAE) network, and convolutional neural networks (CNNs).

Toward the WT gearbox faults, Qin et al. (2018) proposed a novel fault diagnosis method that combines DBNs and improved logical Sigmoid unit for the WT gearbox. The integrated approach, which uses the optimized Morlet wavelet transform, kurtosis index, and soft-thresholding is used to extract impulse components from original signals to advance the accuracy of dignosis. Compared with the traditional Sigmoid method, the WT gearbox fault diagnosis method based on deep confidence network and improved logical Sigmoid unit has the higher comprehensive performance. To achieve anomaly diagnosis and fault analysis of WT components, Zhao et al. (2018) proposed a deep learning method based on DAE networks using the WT SCADA data, while the Boltzmann machine builds a deep automatic encoder network model. This method can realize the early warning of the faulty component and derive the physical location of the faulty WT component through the residual of the deep autoencoder network model. Since the diverse operating status of WT with a large amount of noise interference, which leads to a decrease in the accuracy of fault diagnosis of WT. To solve this problem, Chang et al. (2020) proposed a fault diagnosis method for WT based on a concurrent convolution neural network (CeCNN). The raw WT data do not require any prior knowledge, and the characteristics are learned adaptively and directly from input with high accuracy and powerful generalization ability. The convolutional layers of different branches select kernels of varying scales at the same level, thereby improving the accuracy of the WT fault diagnosis. Yi and Jiang (2020) proposed a DAE-based discriminative feature learning for WT blade icing fault detection.

Although deep learning has a strong learning ability and high fault diagnosis accuracy, it requires extensive data and computing power with high cost and high hardware requirements, which are current issues should be considered.

The FCM algorithm (Bezdek et al., 1984; Pal et al., 2005) is a clustering algorithm in which each data point can belong to more than one cluster. The basic idea is to maximize the similarity between objects divided into the same cluster while minimizing the similarity between viaroius clusters.

Given the WT gearboxes fault detection, Luo et al.(Luo and Huang, 2014) proposed a fault diagnosis method based on global local mean decomposition and FCM clustering. In this method, the known sample was clustered using the FCM clustering, and the test sample was classified and recognized, which has simple implementation and good diagnosis results. Although the WT fault diagnosis methods require supervision and training based on historical samples of known faults, collecting samples of known faults is time-consuming and expensive. Given the lack of complete characteristics of known samples in WT, Li et al. (2015) presented a method based on the kernel FCM (KFCM)clustering to the fault diagnosis of the WT gearbox. The KFCM clustering algorithm is used to classify the samples of known samples, and the classification center of each known fault is obtained. Similarity parameters are also calculated to diagnose whether the new data samples belong to the known faults. This method can accurately and effectively diagnose the known and unknown faults of WT.

Some issues should be considered when the FCM algorithm is used in the WT fault diagnosis. For example, a large fault-free sample size and extremely small fault sample size may lead to failure, thereby ensuring that the optimal solution of the fault diagnosis model is found.

Hierarchical clustering (Johnson, 1967; Corpet, 1988) is a cluster analysis method in unsupervised learning, which builds a model by establishing a hierarchical structure of clusters. The hierarchical clustering method can be represented as a tree structure (i.e., “tree diagram”, which includes roots and leaves. In clustering tree species, the original data points of different categories are the lowest level of the tree, and the top level of the tree is the root node of a cluster. As shown in Figure 9, the hierarchical clustering method (Navarro et al., 1997) involves a process that starts from the leaves and successively merges clusters called agglomerative hierarchical clustering; or a process that begins from the root and recursively splits the clusters called divisive hierarchical clustering. The hierarchical clustering method uses Euclidean distance to calculate the distance between the data points of different categories.

Li Y. et al. (2018) proposed a fault diagnosis method based on adaptive multi-scale morphological filters and improved hierarchical arrangement entropy to identify varoius health situations of gearboxes, and used the hierarchical aggregation method to reduce noise fault features extracted from the signal. Liu and Ge (2018) presented a weighted random forest scheme based on hierarchical clustering selection for fault classification in complex industrial processes. The application of the hierarchical clustering method to offline model selection in RF can reduce the complexity of online fault classification.

In the fault diagnosis process of WT, the need to calculate the proximity matrix in the hierarchical clustering algorithm, is time-consuming, and unsuitable for use in the WT big data sets. Hierarchical clustering method is appropriate for the clustering of small data sets, and real-time issues should be considered when dealing with the WT big data.

The Gaussian mixture model (GMM) (Reynolds, 2009) assumes that all data points conform to the Gaussian distribution, and is generated from a mixed finite number of probability models with unknown parameters. GMM can be regarded as the process of fitting a linear combination of multiple Gaussian distribution functions to perform data distribution.

Heyns et al. (2012) proposed a Gaussian hybrid model to detect WT gearbox failures and calculate the negative log-likelihood of the gearbox bearing vibration signal segment, which represents the healthy gearbox. This method is suitable for nonlinear and non-stationary wind turbine gearbox vibration signals. Given the highly complex and unstable operating conditions of WT, Dong et al. (2013) proposed a multi-parameter WT health assessment framework that considers dynamic operating conditions. After the characteristic parameter selection and GMM based multi-regime modeling, the operation status of WT can be evaluated, which can effectively detect WT faults. In response to frequent WT faults, Luis et al. (Avendaño-Valencia et al., 2017) proposed a fault diagnosis method for WT based on a GMM random coefficient model. The vibration response signals of WT that change with time under the environment and operating conditions are extracted and the model coefficients are determined through the GMM random coefficient framework. The method offering significant performance improvements and most fault levels and types are represented to be correctly diagnosed.

GMM is effective in handling the big volume of WT data samples, but it has a large calculation amount and slow convergence. Selecting the number of sub-models in advance is difficult and is sensitive to abnormal points. When processing small data set samples in WT, the result cannot meet the requirements.

S3VM (Bennett and Demiriz, 1998) involves the development of SVM in semi-supervised learning (Wang et al., 2017). The major idea of S3VM is to mark unlabeled samples to maximize the interval after the hyperplane is divided. The frequently used S3VM is a transductive SVM (TSVM). The basic idea of this method can be presented as five steps. The first step involves training an SVM classifier with labeled samples. The second step entails using SVM to predict the classification results of unlabeled data. The third step aims to find the opposite label in the predicted unlabeled data that may be wrong for the labeled sample to swap the label, and use the existing labeled sample and unlabeled sample to retrain SVM; The fourth step involves repeating the second and third steps until the best S3VM classifier is obtained. The fifth step entails using use the S3VM classifier to label the unlabeled samples and predict the classification results.

The S3VM methods are widely used in the field of WT fault diagnosis. Liu C. et al. (2020) proposed a fault diagnosis method for rolling bearing based on S3VM using only a few labeled samples to build a model with good classification effect. In order to reduce false alarm rates and improve the discriminative ability of incipient fault features, Mao et al. (2020) proposed an online method for early fault detection of bearings using a semi-supervised architecture. A safe semi-supervised SVM (S4VM) is introduced to identify the sequentially arrived data of the goal bearing as anomalous or normals and fault states and a stacked noise reduction automatic encoder is used to extract depth features from the normal state data and fault state data of the bearing. According to the S4VM generalization error upper bound to adaptively identify the occurrence of an incipient fault. Optimal margin distribution learning machine (ODM), which is also classified based on split hyperplane, has also appeared semi-supervised ODM(ssODM) in recent years, and has been applied to wind turbine fault detection with good performance (Zhang T. and Zhou Z.-H., 2018).

S3VM predicts the unlabeled samples, adds the prediction results to the labeled data set and improves the fault diagnosis rate. However, S3VM should determine a few known WT data samples as a guide. Accordingly, we cannot ensure such WT data samples with delicate information and it is uncertain to know how many WT data samples are needed to achieve an effective S3VM model.

The main idea of the semi-supervised generative model is that the probability that unlabeled samples belong to each category as a set of missing parameters. Thereafter, the expectation maximization (EM) algorithm is used to perform maximum likelihood estimate on the parameters of the generated model. Generative model methods (Zhu, 2005; Kingma et al., 2014) include mixed Gaussian distribution, mixture multinomial distribution, and hidden Markov model.

Ge et al.(Xin et al., 2018) proposed a semi-automatic fault detection method based on a probabilistic model in the form of a hybrid Gaussian with good robustness. Wang et al. (Wang et al., 2015) proposed a comprehensive method based on semi-supervised learning, using a small amount of labeled data and a large amount of unlabeled process data to construct a neighborhood weighted graph. By solving the optimization problem, the optimal regression function and the optimal prediction label matrix of unlabeled data are acquired. This method can obtain the promised results of fault detection and fault diagnosis in the monitoring process. To achieve automatic detections, Omid et al. (Geramifard et al., 2013) introduced a semi-parametric method based on the hidden Markov model for fault detection and diagnosis of synchronous motors. After training the hidden Markov model classifier (parameter stage), which is based on each probabilistic (non-parametric stage) hidden Markov model. Moreover, the probabilistic inference are used to compute two matrices to solve the efficiency problem in the fault classification process. Li X. et al. (2018) presented a fault detection method on a multivariate Bayesian control scheme and a hidden semi-Markov model to predict early bearing failures of gearboxes. The method of using the continuous-time hidden semi-Markov decision process to characterize the failure process of the gearbox bearing system, which can predict the early failure of the gearbox bearing and detect the remaining useful life at each sampling epoch.

The semi-supervised generative model method has good robustness, but the fault diagnosis model has low accuracy, long model training time, and many iterations. These issues must be considered in WT fault diagnosis.