# Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
## 5 Conclusions and Future Work  
In summary, we have presented a method for detecting anomalies of brake operating units on the metro vehicle using an LSTM autoencoder-based one-class classifier. In our proposed framework, we first extracted BC pressure data from the BOU data. After that, the extracted BC pressure data is split into subsequences which are fed into our proposed LSTM autoencoder model which consists of two LSTM blocks (encoder and decoder). The LSTM autoencoder model is trained using training data which only consists of normal subsequences. To detect anomalies from the test data that contains abnormal subsequences, the mean absolute error (MAE) on the test data is calculated. The test sequences with MAE larger than the predefined threshold are regarded as an anomaly. We conducted the experiments with the BOU data of metro trains in Korea. Experimental result demonstrates that our proposed model can detect anomalies of the BOU data well. The result indicated that our proposed method using LSTM autoencoder is suitable for detecting anomalies of BOU on metro vehicles. Although the performance of our proposed method is promising, further research needs to be done to tackle the limitations of our method which are (1) it cannot be used for a real-time system since the inference time is long and (2) the number of abnormal cases in BOU dataset is very small. Therefore, we plan to reduce the size of the model using knowledge distillation approaches in order to reduce the inference time and deploy a real-time anomaly detection system. Also, we plan to use data augmentation techniques to generate more abnormal cases to build a more robust model. In addition, we plan to develop an anomaly detection model for another type of BOU anomaly case defined as when the BC pressure is greater than the AC (air cylinder) pressure in some sections where the braking pressure rises before stopping.  
Conceptualization, J.G.; methodology, J.K. and J.G.; software, J.K. and J.G.; validation, J.K. and J.G.; formal analysis, J.K. and J.G.; investigation, J.K. and J.G.; resources, C.-S.K. and J.G.; data curation, C.-S.K. and J.W.K.; writing--original draft preparation, J.K., C.-S.K., J.W.K. and J.G.; writing--review and editing, J.K. and J.G.; visualization, J.K.; supervision, J.G.; project administration, C.-S.K. and J.W.K.; funding acquisition, C.-S.K. and J.W.K. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant21 QPWO-B152223-03).  
Not applicable.  
The authors would like to thank [PERSON] for his assistance in acquiring the BOU dataset.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Detection Accuracy** \\ \hline
1D-CNN-AE & 0.6229 & 0.8036 & 0.7018 & 0.6667 \\ biGRU-AE & 0.8270 & 0.8180 & 0.8225 & 0.7778 \\ biLSTM-AE & 0.8381 & 0.8396 & 0.8389 & 0.8333 \\ GRU-AE & 0.9341 & 0.8432 & 0.8864 & 0.8889 \\ LSTM-AE & **0.9794** & **0.8577** & **0.9145** & **0.9444** \\ \hline \hline \end{tabular} The bold text represents the highest score achieved for each evaluation metric.  
\end{table}
Table 1: Precision, recall, F1-score, and detection accuracy of our proposed model and other autoencoder-based models.