# Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model
## 5 Discussion  
By conducting RFE to remove irrelevant and redundant information from full operational SCADA data, 33 top-ranked features in Figure 5 are reserved for fault predictions.  
LSTM has been preferable for prognostics on imbalanced data owing to its ability to store the time-lagged information and exploit the time dependency. LSTM has better recall scores (both MARs and MIRs) and overall predictions than traditional ML classifiers with respect to the results in Sections 4.1 and 4.2. However, there is also a dependence across time among data, and the time feature of inputs can be either synchronous or  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Scores**} & \multicolumn{5}{c}{**Classifier (PcsTrip)**} \\ \multicolumn{2}{c}{} & **LSTM** & **T2V-LSTM** & **RF** & **ANN** & **SVM** & **KNN** & **ALL** \\ \hline \multirow{2}{*}{MIP} & MIN & 0.8 & 0.8024691 & **0.92308** & 0.77922 & 0.78 & 0.75342 & 0.75342 \\  & MAX & 0.95238 & 0.969697 & 0.98361 & **1** & 0.97826 & 0.96552 & 1 \\ \hline \multirow{2}{*}{MIR} & MIN & 0.74074 & **0.7530864** & 0.65432 & 0.62963 & 0.48148 & 0.62963 & 0.48148 \\  & MAX & 0.83951 & **0.8888889** & 0.81481 & 0.81481 & 0.75309 & 0.79012 & 0.88889 \\ \hline \multirow{2}{*}{F-score} & MIN & 0.7871 & **0.8024691** & 0.78519 & 0.75177 & 0.59542 & 0.71429 & 0.59542 \\  & MAX & 0.88312 & **0.9056604** & 0.88591 & 0.88 & 0.80263 & 0.81013 & 0.90566 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Validation scores for PcsTrip.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Scores**} & \multicolumn{5}{c}{**Classifier (Pitch Fatal Faults)**} \\ \multicolumn{2}{c}{} & **LSTM** & **T2V-LSTM** & **RF** & **ANN** & **SVM** & **KNN** & **ALL** \\ \hline \multirow{2}{*}{MIP} & MIN & 0.53548 & 0.5419355 & 0.64286 & 0.53409 & 0 & **0.69412** & 0 \\  & MAX & 0.79661 & 0.8076923 & **0.8871** & 0.82692 & 0.7931 & 0.82222 & 0.8871 \\ \hline \multirow{2}{*}{MIR} & MIN & 0.4026 & **0.4415584** & 0.31169 & 0.22078 & 0 & 0.33117 & 0 \\  & MAX & 0.66883 & **0.6948052** & 0.50649 & 0.58442 & 0.45455 & 0.48052 & 0.69481 \\ \hline \multirow{2}{*}{F-score} & MIN & 0.50196 & **0.5291829** & 0.43439 & 0.33663 & 0.03704 & 0.45133 & 0.03704 \\  & MAX & 0.70383 & **0.7430556** & 0.60938 & 0.66176 & 0.54902 & 0.60656 & 0.74306 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Validation scores for pitch fatal faults.  
asynchronous [33], but vanilla LSTM always fails to recognise time itself as a feature by assuming all inputs to be synchronous. As the data points related to downtimes or maintenance actions are removed, the modelling dataset is asynchronous. Hence, the Time2 Vec is adopted to remodel the LSTM architectures into T2V-LSTM (see Figure 7) by way of Time2 Vec consuming the time feature under non-stationary activation functions.  
The Time2 Vec activation function and the other hidden layer of T2V-LSTM in Figure 7 are chosen by _Tanh_, which maps the inputs into a range \((-1,1)\). Like _Sigmoid_, the derivative of _Tanh_ is expressed by itself, but the mapping range of _Tanh_ is broader than that of _Sigmoid_ (0, 1). The classification output layer is selected by _Softmax_ because its calculated probabilities determine the target classes with given inputs, chiefly implemented for multi-level classifications. The proposed T2V-LSTM model (with a Time2 Vec function of _Tanh_) has been certified to upgrade the prediction accuracy of LSTM and outperform all other classifiers on both overall and individual fault predictions.