## 6 Conclusions  
In this project, three faults were simulated, namely, open circuit, intermittent open circuit fault, and jamming faults, in an aircraft's electric braking system. The characteristics of the faults were analysed, and the input features for the models were selected. Following  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Average Precision** & **Average Recall** & **Average F1-Score** \\ \hline LSTM Univariate & 77.1\% & 90.0\% & 83.0\% \\ TSF Univariate & 100\% & 100\% & 100\% \\ k-NN Univariate & 61.6\% & 42.1\% & 49.3\% \\ LSTM Multivariate & 20.0\% & 60.0\% & 30.0\% \\ TSF Multivariate & 31.9\% & 51.3\% & 38.7\% \\ k-NN Multivariate & 20.0\% & 12.5\% & 15.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Performance Parameters for Jamming Classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Average Precision** & **Average Recall** & **Average F1-Score** \\ \hline LSTM Univariate & 92.8\% & 100\% & 96.0\% \\ TSF Univariate & 100\% & 98.3\% & 99.1\% \\ k-NN Univariate & 100\% & 89.8\% & 94.4\% \\ LSTM Multivariate & 10.7\% & 40.0\% & 16.9\% \\ TSF Multivariate & 27.0\% & 35.7\% & 30.5\% \\ k-NN Multivariate & 20.0\% & 28.6\% & 23.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Performance Parameters for IOC Classification.  
this, an LSTM model was developed, and its performance was compared against k-NN and TSF univariate and multivariate models.  
There are numerous factors to keep in mind while selecting the appropriate model for identifying a fault. For identifying faults that are equally likely to occur, accuracy might be a good indicator, and so employing the univariate TSF machine learning algorithm is recommended because it provides the highest accuracy. In real life, though, the faults rarely occur in symmetric distribution, and so accuracy is not an ideal indicator to measure the performance of the model.  
Electrical faults are highly critical, and so it will be more cost-efficient to correctly identify all faults, even if the predictor occasionally generates false positives. Henceforth, it would be ideal to choose models that have high recall metrics, which in this case is the TSF univariate model for OC faults and LSTM univariate for IOC faults. Jamming faults occur less frequently and have a criticality factor less than that of electrical faults, and so precision might be of greater importance. Here, TSF univariate models provide both high precision and recall (100%) in identifying jamming faults, making it the ideal choice.  
The decision on what metric is appropriate to evaluate the performance depends on whether the cost incurred due to failure to detect a fault is greater or if the cost associated with grounding an aircraft due to misidentification is more expensive. In the unlikely case that both are equally costly, F1-score is a good metric used to select the model. The TSF univariate model offers the best F1-score for all three faults, making it the obvious choice in this case.  
OEMs or airline providers can also choose to either implement multiple algorithms with each dedicated to identifying a specific fault or choose a single model that is selected after ranking the faults depending on their criticality and probability of occurrence and choosing the appropriate metric to select the model.