# Machine Failure Prediction Using Survival Analysis
## 7 Conclusions  
The breakdown of machines is a major issue in today's technology-dependent societies. To better manage this problem, we propose a machine failure prediction model based on \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Machine Failure**} & \multirow{2}{*}{**HDF**} & \multirow{2}{*}{**OSF**} & \multirow{2}{*}{**PWF**} & \multirow{2}{*}{**RNF**} & \multirow{2}{*}{**TWF**} \\ \cline{1-1} \cline{6-7}  & & & & & & \\ \hline RSF & 2.609\% & 0.01\% & 0\% & 1.508\% & 14.152\% & 25.656\% \\ RF & 0.757\% & \(-\)0.455\% & 0.02\% & 0.1\% & 22.937\% & \(-\)0.054\% \\ COX PH & 1.744\% & \(-\)2.732\% & \(-\)0.02\% & 0\% & 119.9\% & 0.771\% \\ ST & 0.344\% & \(-\)31.277\% & 6.043\% & 2.174\% & \(-\)0.1\% & 8.951\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage of increase/decrease in C-index score after the feature analysis/selection layer. survival analysis techniques. The developed model filters the main features that contribute to machine failure through RSF, COX regression, Rank2D, and RFE techniques. Then, the selected data are fed into the RSF model, which is used to perform the prediction. Using RSF in this scenario provides better management of censored data, as machines failure datasets are often heavily right-censored. The model was subjected to a comparative analysis against a conventional random forest model, survival trees model, and COX PH survival analysis model. It exhibited superior performance in two out of the six categories, while consistently producing reliable predictions, and demonstrating comparable C-index scores with the top-performing models, on the rest of the categories. Additionally, the use of this model enables support for high-dimensional data, which is a common occurrence in machine breakdown. The model showed promising prediction results with a success rate of approximately 97%, while also demonstrating a high ability to predict the cause of the failure of the machine. The high level of accuracy achieved by the proposed approach makes it an especially valuable tool for the development of fault-tolerant systems in large-scale environments, including those within the Internet of Things (IoT). The ability to accurately predict machine failure can help to prevent costly downtime and minimize the risk of catastrophic system failures, thus enhancing the overall reliability and stability of these complex systems. The proposed approach provides a promising solution for the performance optimization and security enhancement of large-scale systems, with potential benefits spanning a wide range of industries and applications. While our study provides valuable insights into the performance of the predictive models in the specific dataset used, there are limitations that warrant further research. Future work could involve the evaluation of our proposed approach on a variety of different datasets to assess its generalizability and robustness. Additionally, further optimization of the feature analysis and selection layer could be considered. These efforts could lead to the development of an even more accurate and reliable predictive model. Conceptualization, D.P., K.D. and N.T. ; methodology, D.P., K.D. and N.T. ; software, D.P. ; validation, D.P. ; formal analysis, D.P., K.D. and N.T. ; investigation, D.P. ; data curation, D.P. and K.D. ; writing--original draft preparation, D.P., K.D. and N.T. ; writing--review and editing, D.P. and K.D. ; visualization, D.P., K.D. and N.T. ; supervision, K.D. ; project administration, K.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.