# Real-Time and Robust Hydraulic System Fault Detection via Edge Computing
## 9 Conclusions  
The challenges in fault detection for hydraulic systems include maintaining real-time fault detection, learning multivariate time-series sensor data, and handling noisy data. To address these issues, we propose a real-time and robust fault detection approach through cooperation between cloud and edge servers. The cloud conducts feature selection and offline learning, while the edge processes online detection near the data source to reduce latency and transmission costs. We outline a new method leveraging GA-based feature selection to identify feature-to-label correlations and feature-to-feature redundancies. By using fewer important features that are transmitted to the edge and processed, detection time is reduced, and model performance is improved. Finally, we describe an LSTM-AE as a robust fault detection model that fully uses temporal information on time-series sensor data that is capable of handling noisy data. The experimental results suggested that our method outperforms previous approaches as we obtain lower detection times, higher accuracies, and more robust performance in the presence of noisy data. With a 63% reduction in features, from 17 to 5-7, our model still attains high accuracy above 98% while being robust to noisy data when the signal-to-noise ratio is approximately 0 dB. Our method also maintains low detection times with an average of 9.42 ms and reduces the packet size to 179.98 KB from a maximum of 343.78 KB.  
Figure 10: Experiments on fault detection with multiple algorithms spanning a range of noise levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Deep Learning Model** & **Total Parameters** & **Model Size (KB)** \\ \hline DNN [23] & 52,351 & 227 \\ CNN [27] & 16,189,496 & 63,275 \\ LSTM-AE (CLF) & 34,584 & 169 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Complexity of the deep learning models.  
For future work, we will first consider other deep learning models, such as CNN combined with LSTM, to offer a better learning model for spatio-temporal data. Then, we will apply this proposed method to other real-world datasets or systems. Finally, we will consider incomplete sensor data issues that could arise from hardware malfunctions or during device replacement.  
Conceptualization, D.Z.F.; Methodology, D.Z.F.; Implementation, D.Z.F.; Validation, D.Z.F. and S.-H.C.; Formal analysis, D.Z.F. and S.-H.C.; Investigation, D.Z.F. and S.-H.C.; Resources, D.Z.F. and S.-H.C.; Data curation, D.Z.F.; Writing--original draft preparation, D.Z.F.; Writing--review and editing, D.Z.F. and S.-H.C.; Visualization, D.Z.F.; Supervision, S.-H.C.; Project administration, S.-H.C.; Funding acquisition, S.-H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2018R1D1A1B07049355) and Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-01450, Artificial Intelligence Convergence Research Center [Pusan National University]).  
The authors declare no conflict of interest.