## 6 Predictive Model Experimental Results
### Discussion and Future Work  
The experimental results show that meaningful real data could only be gathered when equipment operates. We used our real time data to execute our LSTM algorithm. Based on the calculated performance accuracy with a low training and test MAE and RMSE, as Table 2,  
* Our models' accuracy are 90% and 96% on predicting the component temperature. * Our models do not overfit and the loss is less. We naively conclude that their prediction accuracy are good. * We recommend LSTM model to be used as predictive model in proposed structure. Classification of components health status should be done after investigating their operational principals and observing their generated data versus their health change. Based on the pumps operational specification, past maintenance history, as well as the experimental observation on the machine fluctuation versus the changes in pump's temperature, the temperature working performance range was sub-categorized, relating to related operational health status. The temperature from steam is irregular time series, due to the fact that it is usually affected by the normal operation of the machine, such as adding cold water into steam generator and automatic regulated heating grasp during operation. Nevertheless, its output temperature may rise and fall in some range along the operation period. In additional, because the change in temperature of steam relates to the temperature change in sterilization room, we have observed 100 sterilization cycles for classifying the range of desirable temperature of the steam. Table 3 illustrates the operational performance classes with their related temperature range. Figure 15: Actual and predicted results for heaters and pumps models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component Health Status** & **Temperature Range for Pumps** & **Temperature Range for Heaters** \\ \hline Healthy & Below 40 \({}^{\circ}\)C & Above 150 \({}^{\circ}\)C \\ Alerting & 41 to 70 \({}^{\circ}\)C & 141 to 150 \({}^{\circ}\)C \\ Going to collapse & Above 70 \({}^{\circ}\)C & Below 140 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Component health status thresholds for both pumps and heaters. Based on the highlighted equipment' components health category boundaries, Figure 16 shows the process of achieving the final stage of predictive maintenance. On the top of the output layer, every output at current time step will be fed to the defined function with if, elseif, and else condition to read the output and decide based on settled thresholds with the purpose of classifying three health states' labels of the component as Healthy, Alerting, or Going to collapse. For the alerting system, it is found that, for heaters, temperatures below 150 are only occurring in preheating time that only takes up to five minutes; since the data readings are two per minute, the maximum readings below 150 \({}^{\circ}\)C could not go beyond 10 along the whole sterilization cycle. Similarly for pumps, occurrence beyond a healthy status is a good indicator of defect. The maintenance team need to be ready and take actions for maintenance activities, since the 'Alerting' reading is reported and it performs activities as soon as 'Going to collapse' status is reported. Because a complete sterilization cycle may vary from 45 to 55 min, depending on the load in chamber, the function is defined to conclude the component status after each operation cycle, which is averaged to 50 min. The maintenance team will get an alerting message, since the concluded status is alerted to get ready for the next repair state. For future work in this field, we note that there are many components on single equipment and different physical parameters on each component that could be assessed in order to create full operation status monitoring of the equipment. Additional works would consider a multivariable monitoring that combines different component physical parameters.