# Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
## 4 Implementation  
Prototype software was implemented for running the proposed approach in real time. The implementation and evaluation were carried out by using the MATLAB(r) R2019a environment in a 64-bit Windows(r) 10 operating system. MATLAB(r) is a proprietary multi-paradigm programming language and numerical computing environment developed by MathWorks, Inc. (Natick, Massachusetts, United States of America). The _Symbolic Math Toolbox_, the _Signal Processing Toolbox_, the _Statistics and Machine Learning Toolbox_, and the _Statistics and Machine Learning Toolbox_ were acquired to execute the method and perform the MLE for prediction confidence assessment. The source code was wrapped into a standalone executable package that runs real-time prognostics in the machine. The standalone package also requires the _Data Acquisition Toolbox_ for creating and managing the data acquisition interface. An illustration of the system's architecture is presented in Figure 2(a), while in Figure 2(b), examples of the Graphical User Interface (GUI) indications are portrayed. The GUI shows  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Confidence Score** & **Assigned Labels** \\ \hline
100\% & Sure prediction & “Maximum” \\
85\%–99.9\% & Extremely reliable prediction & “Extremely High” \\
70\%–85\% & Very reliable prediction & “Very High” \\
50\%–70\% & Reliable prediction & “High” \\
30\%–50\% & Moderate prediction & “Moderate” \\ \textless{}30\% & Unreliable prediction & “Poor” \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction confidence assessment scale and assigned labels.  
how many pieces have been produced by the used segments, the interval RUL forecast, and the forecast confidence.  
The RUL prognosis application is designed to run in a PC outside the rolling mill machine. It was developed for exclusive use in the study of the rolling mill machine. Data from the machine's sensor are provided from a database. The application offers the option to change three parameters: (1) the allowable embodied energy capacity, (2) the update horizon, and (3) the prediction tolerance mentioned in Equation (4) of Section 3. All can be adjusted from a configuration file. In the configuration file, the user determines the energy/wear level that the industry wants to replace the segments. This user-tuned parameter should not be higher than the energy level that corresponds to segment breakage, since it will generate invalid predictions. In the second line, the update rate is defined. For instance, a horizon value of 1 will provide RUL prediction updates after every single processed product, while a value of 100 will provide updates after every 100 products. For the third configuration parameter, the prediction tolerance is defined. Value selection of this parameter is subjective for each use case and depends on how strict or lenient forecast is demanded. A very low prediction tolerance increases prediction uncertainty. On the other hand, a tolerance value that is too high may lead to earlier and long-hour vigilance for carrying out maintenance. Taking into account the net processing time of each product can help define prediction tolerance, and thus avoid these issues.