# Fault Diagnosis Approach of Main Drive Chain in Wind Turbine Based on Data Fusion
## 3 Fault diagnosis of Wind Turbine Main Drive Chain Based on Fusion of Two Types of Data  
Based on low-frequency fault and high-frequency fault features obtained from the fusion of two types of data and fault characteristic variables obtained by combining the main shaft and generator rotation speed of the wind turbine and the operation control mode of the wind turbine, dozens of typical characteristics of fault early warning are generated after dimensionality reduction. However, they are still multi-variable and large scale data. Moreover, different typical faults have a different number of characteristics of fault early warning. To deal with this kind of multi-variable fault diagnosis problem that input variables need to be adjusted for different typical faults, the Deep Autoencoder (DA) model is a suitable approach for the fault diagnosis model training of different typical fault types. The training of the fault diagnosis model for typical fault types mainly includes the following steps:  
1. Select the low-frequency monitoring data from the SCADA system and the high-frequency vibration monitoring data from the main drive chain vibration fault diagnosis system under the normal state and a typical fault state of the wind turbine main drive chain. Calculate the characteristics of a fault warning and establish a sample data set of this typical fault. Normalize the sample data set and divide it into a training set and test set with a certain proportion. 2. Determine the number of stacked AEs (Auto Encoders) and establish the DA with multiple hidden layers. The number of input layer neurons is the dimension of the input sample, and the data set is used for pre-training by stacking AEs. 3. Use the labeled samples in the main drive chain training data set to apply supervised fine-tuning to the entire DA to complete all training processes. 4. When the entire DA training is completed, establish the DA model for the main drive chain, calculate the reconstruction error R with the test sample set, and integrate the test samples into the DA model for testing. The fault diagnosis model training process of typical fault types is shown in Figure 5.  
Figure 5: Process of generating DA-based fault diagnosis model. The DA model training process above is unsupervised learning of the sample data set. The parameters obtained by training can be used as prior information of supervised learning of the DA model. The DA model can be further optimized by using the labeled data set for supervised learning to improve accuracy of fault diagnosis. This fine-tuning process is designed as follows:  
Assume that the sample data are:  
\[\left\{\left(x^{(1)},y^{(2)}\right),\ \cdots,\ \left(x^{(i)},y^{(i)}\right),\ \cdots\ \left(x^{(m)},y^{(m)}\right)\right\}_{i=1}^{m} \tag{5}\]  
where the category status corresponding to \(x_{i}\) is \(y^{(i)}\in\{1,\ 2,\ \cdots,\ k\}\), which is generally is given in the form of label encoder, and k represents the total number of categories. According to the analysis above, the \(k\)-dimensional vector output obtained by the classifier represents the conditional probability \(h_{\theta}\left(x^{(i)}\right)=p(y=j\big{|}x)\) that the input x is the corresponding category, and the main form is:  
\[h_{\theta}\left(x^{(i)}\right)=\left[ \begin{array}{c}p(y^{(i)}=1\big{|}x^{( i)};\theta)\\ p(y^{(i)}=2\big{|}x^{(i)};\theta)\\ \cdots\\ p(y^{(i)}=k\big{|}x^{(i)};\theta)\end{array} \right]=\frac{1}{\sum_{j=1}^{k}e^ {\theta_{j}^{T}x(i)}}\left[ \begin{array}{c}e^{\theta_{1}^{T}x(i)}\\ e^{\theta_{2}^{T}x(i)}\\ \cdots\\ e^{\theta_{k}^{T}x(i)}\end{array} \right] \tag{6}\]  
\(\theta\) is not for the main chain in the software uses the data-fusion-based fault diagnosis approach we discussed in this article. For the typical fault of a gearbox with broken tooth in the main drive chain, the actual data of the wind turbine from 2 May to 29 June 2019 are used to calculate the fault features based on the fusion of the two types of data, and the result is 21 fault warning characteristic variables.