# Anticipating Future Behavior of an Industrial Press Using LSTM Networks
## 7 Conclusions  
Predicting industrial machines' future behaviors is key for predictive maintenance success. The present research aims to find prediction models adequate for anticipating the future behavior of industrial equipment with good certainty.  
The predictive model used was based on LSTM networks, with encoding and decoding layers as the input and output, respectively. In this study, different data pre-processing techniques, network architectures, and hyperparameters were tested, in order to determine the best models.  
The predictive model used was based on LSTM network, with encoding and decoding layers as the input and output, respectively.  
The results show that the model proposed is able to learn and forecast the behavior of the six variables studied: torque, pressure, current intensity, velocity, oil level and temperature. The best results were obtained using a window of samples of the last 10 days at two samples per day. The MAPE errors varied in the range of 2 to 17% for one of the best models for different variables.  
Future work includes additional experiments to determine the optimal sampling rate and stabilize the results for optimal performance with all the variables. The predicted results will also be used to determine an expected probability of failure, using classification models. Other methods may also be used to deal with discrepant data. Later, the models developed will also be applied to other equipment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Window Size (Days)** & **Train** & **Test** & **Units** & **Train** & **Test** \\ \hline
5 & 8.91 & 8.87 & 10 & 2.07 & 1.99 \\
10 & 1.80 & 1.61 & 20 & 1.82 & 1.91 \\
20 & 3.29 & 3.23 & 30 & 1.65 & 1.59 \\
30 & 1.98 & 1.94 & 40 & 1.58 & 1.41 \\
40 & 2.07 & 1.98 & 50 & 1.61 & 1.42 \\
50 & 4.32 & 4.16 & 60 & 1.64 & 1.46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The magnitude of RMSE errors in the test and training set, using two samples per day.  
**Author Contributions:** Conceptualization, J.T.F., A.M.C., M.M.; methodology, J.T.F. and M.M.; software, B.C.M. and M.M.; validation, J.T.F. and M.M.; formal analysis, J.T.F. and M.M.; investigation, B.C.M. and M.M.; resources, T.F., A.M.C. and M.M.M.; writing--original draft preparation, B.C.M.; writing--review and editing, J.T.F. and M.M.; project administration, J.T.F. and A.M.C.; funding acquisition, J.T.F. and A.M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under the [PERSON] grant agreement 871284 project SSHARE and the European Regional Development Fund (ERDF) through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-029494, and by National Funds through the FCT--Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/29494/2017, UIDB/04131/2020, and UIDP/04131/2020.  
**Acknowledgments:** This research is sponsored by FEDER funds through the program COMPETE--Program Operacional Facores de Competitividade--and by national funds through FCT--Fundacao para a Ciencia e a Tecnologia--under the project UIDB/00285/2020. This work was produced with the support of INCD funded by FCT and FEDER under the project 01/SAICT/2016 n\({}^{\circ}\) 022153.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ARMA Autoregressive Integrated Moving Average CNN Convolution Neural Networks LSTM Long Short-Term Memory MAE Mean Absolute Error MAPE Mean Absolute Percentage Error RMSE Root Mean Square Error RNN Recurrent Neural Networks