# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 4 Results  
### Overall Performance Metrics  
In this subsection, the fault prediction models are extracted from timestamps \(t-1\) (10 min) to \(t-21\) (210 min). The detailed predictive performances under six classifiers are summarised in Figure 8 in terms of accuracy, MAP, MAR, and F-score with respect to Equations (2), (23), (24), and (27), respectively. As seen in Table 5, all six classifiers have an upper accuracy of over 94% due to their correct predictions on fault-free cases from the imbalanced dataset. However, the resultant MARs and F-scores have poorer ranges of (0.61156, 0.92622) and (0.71038, 0.93537), respectively, and SVM especially has the poorest MIR range (0.61156, 0.84711). As MAPs (over 0.84) have better ranges than MARs (over 0.61), the resultant F-scores are promoted by precision scores, thereby representing more FNs than FPs. Among all, T2V-LSTM has correctly predicted more fault statuses than other classifiers due to its optimum MAR range. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper Parameter** & **Grid** & **Optimisation** \\ \hline Batch size & 10, 20, 25, 40, 50, 60, 80, 100 & 25 \\ \hline Number of Epochs & 10, 20, 25, 40, 50, 60, 80, 100 & 100 \\ \hline Activation function (\(\mathcal{F}\)) & _Elu, Relu, Sigmoid, Sine_ (only Time2 Vec), _Softmax, Softplus, Softsign, Tanh_ & _Tanh_ \\ \hline Activation function (\(\sigma_{c}\)) & _Elu, Relu, Sigmoid, Softmax, Softplus, Softsign, Tanh_ & _Tanh_ \\ \hline Activation function (\(\sigma_{h}\)) & _Elu, Relu, Sigmoid, Softmax, Softplus, Softsign, Tanh_ & _Softmax_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyper-parameter optimisation through GridSearchCV. Moreover, T2V-LSTM also has the finest ranges of accuracy and F-score despite its poorer MAP range in comparison to RF. As seen in Figure 8, the time index under 10 min per timestamp in the x-axis denotes the test cases at timestamp \(t-n\). All classification approaches demonstrate their best accuracy, MAR, and F-score initially at \(t-1\), but their predictive results progressively attenuate over time. Figure 8: (**a**) Accuracy, (**b**) MAP, (**c**) MAR, and (**d**) F-score for fault predictions under six classifiers from \(t-1\) (10 min) to \(t-21\) (210 min). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Scores**} & \multicolumn{6}{c}{**Classifier (Overall Fault)**} \\ \multicolumn{2}{c}{**LSTM**} & \multicolumn{1}{c}{**T2V-LSTM**} & \multicolumn{1}{c}{**RF**} & \multicolumn{1}{c}{**ANN**} & \multicolumn{1}{c}{**SVM**} & \multicolumn{1}{c}{**KNN**} & \multicolumn{1}{c}{**ALL**} \\ \hline \multirow{2}{*}{Accuracy} & MIN & 0.97079 & **0.9742954** & 0.96222 & 0.96273 & 0.94477 & 0.96284 & 0.94477 \\  & MAX & 0.98441 & **0.9864767** & 0.97791 & 0.9777 & 0.96697 & 0.97213 & 0.98648 \\ \hline \multirow{2}{*}{MAP} & MIN & 0.87643 & 0.8912656 & **0.92785** & 0.86133 & 0.84729 & 0.89391 & 0.84729 \\  & MAX & 0.94347 & 0.9469835 & **0.97052** & 0.9422 & 0.90898 & 0.92044 & 0.97052 \\ \hline \multirow{2}{*}{MAR} & MIN & 0.82667 & **0.8497778** & 0.70844 & 0.74844 & 0.61156 & 0.736 & 0.61156 \\  & MAX & 0.91911 & **0.9262222** & 0.872 & 0.88978 & 0.84711 & 0.84089 & 0.92622 \\ \hline \multirow{2}{*}{F-score} & MIN & 0.86151 & **0.8788294** & 0.80383 & 0.82085 & 0.71038 & 0.81275 & 0.71038 \\  & MAX & 0.92694 & **0.935368** & 0.90707 & 0.9067 & 0.87351 & 0.8739 & 0.93537 \\ \hline \multirow{2}{*}{Execution time (s)} & MIN & 299.0876 & **309.44707** & 281.5733 & 232.2455 & 174.6638 & 148.6676 & 148.6676 \\  & MAX & 337.6192 & **346.63702** & 322.1635 & 281.5004 & 326.7903 & 186.6617 & 346.63702 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Validation scores for overall fault prediction. As seen in Figure 8b, RF has the best MAPs over time, implying its distinction of diagnosing fault-free conditions precisely with fewer FPs. By comparison, T2V-LSTM has successfully predicted more faults than other classifiers due to its relatively highest MARs in all test cases (see Figure 8c).