# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 2 SCADA Data
### Feature Engineering  
The major occurrences of the faults are on the HPU, blade, pitch system, gearbox, PCS, and sub-pitch system (see Table 3), and a huge number of original features are CountFalse/CountTrue states, apparently irrelevant to those faults. This leads to the principal selection of 60 relevant features, which are only a small subsection of the original 2000 features. Among the 60 features, the deviations of pitch angles, as well as the deviations of sub-pitch positions from blades 1 and 2, 2 and 3, and 3 and 1 are considered because of possible blade angle asymmetry or blade angle implausibility, studied by [PERSON] and [PERSON] (2018).  
Feature engineering aims to reduce dimensionality by eliminating features with lower significance and improving the computational efficiency of deep-learning neural networks. RFE ([PERSON] and [PERSON], 2018) has been commonly applied to fit the model by recursively removing irrelevant or redundant features.  
Firstly, an estimator for accurate online fault diagnosis is required to cooperate with RFE for dimensionality reduction. Apart from detecting the abnormality, fault diagnosis can determine the specific fault types with an advanced multi-level fault classification. The accuracy is used to evaluate the performance of classifiers by:  
\[Accuracy=\frac{1}{k}\sum_{i=1}^{k}\frac{TP_{i}+TN_{i}}{TP_{i}+FP_{i}+FN_{i}+TN_ {i}} \tag{2}\]  
where \(k\) is the total number of classes, \(TP_{i}\) donates true positive in class \(i\), when both prediction and actuality are faulty, whilst \(TN_{i}\) donates true negative in class \(i\), when both prediction and actuality are fault-free. \(FP_{i}\) signifies false positive in class \(i\), when the actual fault-free condition is wrongly predicted to be faulty, whilst \(FN_{i}\) signifies false negative in class \(i\), when the actual faulty condition is wrongly predicted to be fault-free.  
ML approaches, such as Decision Tree (DT), \(k\)-NN, SVM, RF, ANN, and Gradient Boost (GB), are compared for fault diagnosis. As seen in Figure 3, RF is evidently the finest model among all in terms of its best accuracy (0.98607386). Hence, the RF classifier is chosen to conduct RFE using a 10-fold cross-validation for the test set. Based on the RFE process in Figure 4, the best accuracy (0.9888) is observed by selecting 33 optimal features out of 60 for the fault diagnosis task under RF classification.  
As seen in Figure 5, the weighted importance of each feature is depicted under the RF classification. According to the optimal solution given in Figure 4, the 33 top-ranking features in Figure 5 are reserved for predictive fault diagnosis models. The features, such as \"AverageMeasuredPtchAngle1_Max\", \"GBoxFilterPres2_Mean\", etc., have advanced significance levels. However, the features with lower significance levels than \"ManualPtchStateCounter_EndVal\" are excluded in predictive fault diagnosis studies for the purpose of dimensionality reduction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Event Code** & **Frequency** & **Description** \\ \hline
0 & 42,598 & Fault-free \\
290 & 565 & HPU 2 pump active for too long \\
399 & 998 & Blade 3 too slow to respond \\
435 & 826 & Pitch system fatal error \\
543 & 522 & Gearbox cooling line pressure too low \\
570 & 1436 & (Demoted) gearbox filter manifold pressure 2 shutdown \\
700 & 701 & PcsOff *1 \\
701 & 417 & PcsTrip *2 \\
1219 & 816 & Sub-pitch priv fatal error has occurred more than 3 times \\ \hline \hline \end{tabular}  
Figure 4: Cross-validation scores plotted against the number of features.  
Figure 3: Comparison of classification accuracy.